Qualcomm Patent | Virtual representation encoding in scene descriptions

where S₀ is a mean 3D shape, π is a selection matrix to obtain the x.y coordinates, z is a constant, R is a rotation matrix from pitch, yaw, roll, and t is a translation vector.

FIG. 11 is a diagram illustrating an example of using a 3DMM fitting curve to drive a virtual representation (or avatar) with a metahuman using the techniques described above.

FIG. 12 is a diagram illustrating an example of an end-to-end flow of a system. The flow of FIG. 12 can represent a general flow that can run end-to-end for 1-to-1 communication between a user device A and a user device B, such as described in U.S. Provisional Application 63/371,714, filed Aug. 17, 2022 and titled “DISTRIBUTED GENERATION OF VIRTUAL CONTENT,” which is hereby incorporated by reference in its entirety and for all purposes. In some cases, the system of FIG. 12 can include an additional server node that can be used for a multi-user scenario.

FIG. 13 is a diagram illustrating an example of performing avatar animation. For example, to improve the realism of avatar animation, various techniques can be performed. For example, the system can estimate a rough (or course) mesh and blend shapes from images (e.g., HMD images) using a 3DMM encoder. The system can improve realism of the textures using an additional facial part neural network, such as using techniques described in U.S. Non-Provisional Application 17/813,556, filed Jul. 19, 2022 and titled “FACIAL TEXTURE SYNTHESIS FOR THREE-DIMENSIONAL MORPHABLE MODELS,” which is hereby incorporated by reference in its entirety and for all purposes. As shown, the facial part network may be a neural network including an encoder and a decoder. In some cases, the facial partnetwork may be a neural network including an encoder only (and not an encoder-decoder), in which case the encoder would output features (e.g., a feature vector or embedding vector) representing the part of an input image corresponding to the face. The system can improve realism of the mesh by deforming mesh vertices for a more personalized mesh, such as using techniques described in U.S. Non-Provisional Application 17/714,743, filed Apr. 6, 2022, which is hereby incorporated by reference in its entirety and for all purposes. In some aspects, audio can also be fused into the input of the system of FIG. 13 to improve realism, such as using techniques described in U.S. Non-Provisional Application 17/930,244, filed Sep. 7, 2022 and in U.S. Non-Provisional Application 17/930,257, filed Sep. 7, 2022,” both of which are hereby incorporated by reference in its entirety and for all purposes.

FIG. 14 is a diagram illustrating an example of an example of an XR system 1400 configured with an avatar call flow directly between client devices, in accordance with aspects of the present disclosure. As shown, FIG. 14 includes a first client device 1402 of a first user and a second client device 1404 of a second user. In one illustrative example, the first client device 1402 is a first XR device (e.g., an HMD configured to display VR, AR, and/or other XR content) and the second client device 1404 is a second XR device (e.g., an HMD configured to display VR, AR, and/or other XR content). While two client devices are shown in FIG. 14, the call flow of FIG. 14 can be between the first client device 1402 and multiple other client devices. The XR system 1400 illustrates the first client device 1402 and the second client device 1404 participating in a virtual session (in some cases with other client devices not shown in FIG. 14). In the example of FIG. 14, the client device 1402 can be considered a source (e.g., a source of information for generating at least one frame for the virtual scene) and the second client device 1404 can be considered a target (e.g., a target for receiving the at least one frame generated for the virtual scene).

As noted above, the information sent by a client device may include information representing a face (e.g., codes or features representing an appearance of the face or other information) of a user of the first client device 1402, information representing a body (e.g., codes or features representing an appearance of the body, a pose of the body, or other information) of the user of the first client device 1402, information representing one or more hands (e.g., codes or features representing an appearance of the hand(s), a pose of the hand(s), or other information) of the user of the first client device 1402, pose information (e.g., a posein six-degrees-of-freedom (6-DOF), referred to as a 6-DOF pose) of the first client device 1402, audio associated with an environment in which the first client device 1402 is located, any combination thereof, and/or other information.

For example, the first computing device 1402 may include a face encoder 1409, geometry encoder engine 1411, a pose engine 1412, a body encoder 1414, a hand engine 1416, and an audio coder 1418. In some aspects, the first client device 1402 may include a body encoder 1414 configured to generate a virtual representation of the user's body. The first client device 1402 may include other components or engines other than those shown in FIG. 14 (e.g., one or more components on the device 500 of FIG. 5, one or more components of the computing system 2000 of FIG. 20, etc.). The second client device 1404 is shown to include a user virtual representation system 1420, audio decoder 1425, spatial audio engine 1426, lip synchronization engine 1428, re-projection engine 1434, a display 1436, and a future pose prediction engine 1438. In some cases, each client device (e.g., first client device 1402, second client device 1402, other client devices) may include a face engine, a pose engine, a body engine, a hand engine, an audio coder (and in some cases an audio decoder or combined audio encoder-decoder), a video decoder (and in some cases a video encoder or combined video encoder-decoder), a re-projection engine, a display, and a future pose prediction engine. These engines or components are not shown in FIG. 14 with respect to the first client device 1402 and the second client device 1404 because the first client device 1402 is a source device and the second client device 1404 is a target device in the example of FIG. 14.

The face encoder 1409 of the first client device 1402 may receive one or more input frames 1415 from one or more cameras of the first client device 1402. For instance, the input frame(s) 1415 received by the face encoder 1409 may include frames (or images) captured by one or more cameras with a field of view of a mouth of the user of the first client device 1402, a left eye of the user, and a right eye of the user. Other images may also be processed by the face encoder 1409. In some cases, the input frame(s) 1415 can be included in a sequence of frames (e.g., a video, a sequence of standalone or still images, etc.). The face encoder 1409 can generate and output a code (e.g., a feature vector or multiple feature vectors) representing a face of the user of the first client device 1402. The face encoder 1409 can transmit the code representing the face of the user to the user virtual representation system 1420 of the second client device 1404. In one illustrative example, the face encoder 1409 may include one or moreface encoders that include one or more machine learning systems (e.g., a deep learning network, such as a deep neural network) trained to represent faces of users with a code or feature vector(s). In some cases, the face encoder 1409 can include a separate encoder for each type of image that the face encoder 1409 processes, such as a first encoder for frames or images of the mouth, a second encoder for frames or images of the right eye, and a third encoder for frames or images of the left eye. The training can include supervised learning (e.g., using labeled images and one or more loss functions, such as mean-squared error MSE)), semi-supervised learning, unsupervised learning, etc.). For instance, a deep neural network may generate and output the code (e.g., a feature vector or multiple feature vectors) representing the face of the user of the first client device 1402. The code can be a latent code (or bitstream) that can be decoded by a face decoder (not shown) of the user virtual representation system 1420 that is trained to decode codes (or feature vector) representing faces of users in order to generate virtual representations of the faces (e.g., a face mesh). For example, the face decoder of the user virtual representation system 1420 can decode the code received from the first client device 1402 to generate a virtual representation of the user's face.

A geometry encoder engine 1411 of the first client device 1402 may generate a 3D model (e.g., a 3D morphable model or 3DMM) of the user's head or face based on the one or more frames 1415. In some aspects, the 3D model can include a representation of a facial expression in a frame from the one or more frames 1415. In one illustrative example, the facial expression representation can be formed from blendshapes. Blendshapes can semantically represent movement of muscles or portions of facial features (e.g., opening/closing of the jaw, raising/lowering of an eyebrow, opening/closing eyes, etc.). In some cases, cach blendshape can be represented by a blendshape coefficient paired with a corresponding blendshape vector. In some examples, the facial model can include a representation of the facial shape of the user in the frame. In some cases, the facial shape can be repsresented by a facial shape coefficient paired with a corresponding facial shape vector. In some implementations, the geometry encoder engine 1411 (e.g., a machine learning model) can be trained (e.g., during a training process) to enforce a consistent faical shape (e.g., consistent facial shape coefficients) for a 3D facial model regardless of a pose (e.g., pitch, yaw, and roll) associated with the 3D facial model. For example, when the 3D facial model is rendered into a 2D image or frame for display, the 3D facial model can be projected onto a 2D image or frame using a projection technique.

In some aspects, the blend shape coefficients may be further refined by additionally introducing a relative pose between the first client device 1402 and the second client device 1404. For instance, the relative pose information may be processed using a neural network to generate view-dependent 3DMM geometry that approximates ground truth geometry. In another example, the face geometry may be determined directly from the input frame(s) 1415, such as by estimating additive vertices residual from the input frame(s) 1415 and producing more accurate facial expression details for texture synthesis.

The pose engine 1412 can determine a pose (e.g., 6-DOF pose) of the first client device 1402 (and thus the head pose of the user of the first client device 1402) in the 3D environment. The 6-DOF pose can be an absolute pose. In some cases, the pose engine 1412 may include a 6-DOF tracker that can track three degrees of rotational data (e.g., including pitch, roll, and yaw) and three degrees of translation data (e.g., a horizontal displacement, a vertical displacement, and depth displacement relative to a reference point). The pose engine 1412 (e.g., the 6-DOF tracker) may receive sensor data as input from one or more sensors. In some cases, the pose engine 1412 includes the one or more sensors. In some examples, the one or more sensors may include one or more inertial measurement units (IMUs) (e.g., accelerometers, gyroscopes, etc.), and the sensor data may include IMU samples from the one or more IMUs. The pose engine 1412 can determine raw pose data based on the sensor data. The raw pose data may include 6DOF data representing the pose of the first client device 1402, such as three-dimensional rotational data (e.g., including pitch, roll, and yaw) and three-dimensional translation data (e.g., a horizontal displacement, a vertical displacement, and depth displacement relative to a reference point).

The body encoder 1414 may receive one or more input frames 1415 from one or more cameras of the first client device 1402. The frames received by the body encoder 1414 and the camera(s) used to capture the frames may be the same or different from the frame(s) received by the face encoder 1409 and the camera(s) used to capture those frames. For instance, the input frame(s) received by the body encoder 1414 may include frames (or images) captured by one or more cameras with a field of view of body (e.g., a portion other than the face, such as a neck, shoulders, torso, lower body, feet, of the user etc.) of the user of the first client device 1402. The body encoder 1414 can perform one or more techniques to output a representation of the body of the user of the first client device 1402. In one example, the body encoder 1414can generate and output a 3D mesh (e.g., including a plurality of vertices, edges, and/or faces in 3D space) representing a shape of the body. In another example, the body encoder 1414 can generate and output a code (e.g., a feature vector or multiple feature vectors) representing a body of the user of the first client device 1402. In one illustrative example, the body encoder 1414 may include one or more body encoders that include one or more machine learning systems (e.g., a deep learning network, such as a deep neural network) trained to represent bodies of users with a code or feature vector(s). The training can include supervised learning (e.g., using labeled images and one or more loss functions, such as MSE), semi-supervised learning, unsupervised learning, etc.). For instance, a deep neural network may generate and output the code (e.g., a feature vector or multiple feature vectors) representing the body of the user of the first client device 1402. The code can be a latent code (or bitstream) that can be decoded by a body decoder (not shown) of the user virtual representation system 1420 that is trained to decode codes (or feature vectors) representing virtual bodies of users in order to generate virtual representations of the bodies (e.g., a body mesh). For example, the body decoder of the user virtual representation system 1420 can decode the code received from the first client device 1402 to generate a virtual representation of the user's body.

The hand engine 1416 may receive one or more input frames 1415 from one or more cameras of the first client device 1402. The frames received by the hand engine 1416 and the camera(s) used to capture the frames may be the same or different from the frame(s) received by the face encoder 1409 and/or the body encoder 1414 and the camera(s) used to capture those frames. For instance, the input frame(s) received by the hand engine 1416 may include frames (or images) captured by one or more cameras with a field of view of hands of the user of the first client device 1402. The hand engine 1416 can perform one or more techniques to output a representation of the one or more hands of the user of the first client device 1402. In one example, the hand engine 1416 can generate and output a 3D mesh (e.g., including a plurality of vertices, edges, and/or faces in 3D space) representing a shape of the hand(s). In another example, the hand engine 1416 can output a code (e.g., a feature vector or multiple feature vectors) representing the one or more hands. For instance, the hand engine 1416 may include one or more body encoders that include one or more machine learning systems (e.g., a deep learning network, such as a deep neural network) that are trained (e.g., using supervised learning, semi-supervised learning, unsupervised learning, etc.) to represent hands of users witha code or feature vector(s). The code can be a latent code (or bitstream) that can be decoded by a hand decoder (not shown) of the animation and scene rendering system 1410 that is trained to decode codes (or feature vectors) representing virtual hands of users in order to generate virtual representations of the bodies (e.g., a body mesh). For example, the hand decoder of the user virtual representation system 1420 can decode the code received from the first client device 1402 to generate a virtual representation of the user's hands (or one hand in some cases).

The audio coder 1418 (e.g., audio encoder or combined audio encoder-decoder) can receive audio data, such as audio obtained using one or more microphones 1417 of the first client device 1402. The audio coder 1418 can encode or compress audio data and transmit the encoded audio to the audio decoder 1425 of the second client device 1404. The audio coder 1418 can perform any type of audio coding to compress the audio data, such as a Modified discrete cosine transform (MDCT) based encoding technique. The encoded audio can be decoded by the audio decoder 1425 of the second client device 1404 that is configured to perform an inverse process of the audio encoding process performed by the audio coder 1418 to obtain decoded (or decompressed) audio.

The user virtual representation system 1420 of the second client device 1404 can receive the input information received from the first client device 1402 and use the input information to generate and/or animate a virtual representation (or avatar) of the user of the first client device 1402. In some aspects, the user virtual representation system 1420 may also use a future predicted pose of the second client device 1404 to generate and/or animate the virtual representation of the user of the first client device 1402. In such aspects, the future pose prediction engine 1438 of the second client device 1404 may predict pose of the second client device 1404 (e.g., corresponding to a predicted head pose, body pose, hand pose, etc. of the user) based on a target pose and transmit the predicted pose to the user virtual representation system 1420. For instance, the future pose prediction engine 1438 may predict the future pose of the second client device 1404 (e.g., corresponding to head position, head orientation, a line of sight such as view 130-A or 130-B of FIG. 1, etc. of the user of the second client device 1404) for a future time (e.g., a time T, which may be the prediction time) according to a model. The future time T can correspond to a time when a target view frame will be output or displayed by the second client device 1404. As used herein, reference to a pose of a client device (e.g.,second client device 1404) and to a head pose, body pose, etc. of a user of the client device can be used interchangeably.

The predicted pose can be useful when generating a virtual representation because, in some cases, virtual objects may appear delayed to a user when compared to an expected view of the objects to the user or compared with real-world objects that the user is viewing (e.g., in an AR, MR, or VR see-through scenario). Referring to FIG. 1 as an illustrative example, without head motion or pose prediction, updating of virtual objects in view 130-B from the previous view 130-A may be delayed until head pose measurements are conducted such that the position, orientation, sizing, etc. of the virtual objects may be updated accordingly. In some cases, the delay may be due to system latency (e.g., end-to-end system delay between second client device 1404 and a system or device rendering the virtual content, such as the user virtual representation system 1420), which may be caused by rendering, time warping, or both. In some cases, such delay may be referred to as round trip latency or dynamic registration error. In some cases, the error may be large enough that the user of second client device 1404 may perform a head motion (e.g., the head motion 115 illustrated in FIG. 1) before a time pose measurement may be ready for display. Thus, it may be beneficial to predict the head motion 115 such that virtual objects associated with the view 130-B can be determined and updated in real-time based on the prediction (e.g., patterns) in the head motion 115.

As noted above, the user virtual representation system 1420 may use the input information received from the first client device 1402 and the future predicted pose information from the future pose prediction engine 1438 to generate and/or animate the virtual representation of the user of the first client device 1402. As noted herein, animation of the virtual representation of the user can include modifying a position, movement, mannerism, or other feature of the virtual representation to match a corresponding position, movement, mannerism, etc. of the user in a real-world or physical space. In some aspects, the user virtual representation system 1420 may be implemented as a deep learning network, such as a neural network (e.g., a convolutional neural network (CNN), an autoencoder, or other type of neural network) trained based on input training data (e.g., codes representing faces of users, codes representing bodies of users, codes representing hands of users, pose information such as 6-DOF pose information of client devices of the users, inverse kinematics information, etc.) usingsupervised learning, semi-supervised learning, unsupervised learning, etc. to generate and/or animate virtual representations of users of client devices.

In some cases, the user virtual representation system 1420 can receive user enrolled data 1450 of the first user (user A) of the first client device 1402 to generate a virtual representation (e.g., an avatar) for the first user. The enrolled data 1450 of the first user can include the mesh information. The mesh information can include information defining the mesh of the avatar of the first user of the first client device 1402 and other assets associated with the avatar (e.g., a normal map, an albedo map, a specular reflection map, etc.). In some cases, mesh animation parameters can include the facial part codes from the face encoder 1409, the face blend shapes from the geometry encoder engine 1411 (which may include a 3DMM head encoder in some cases), the hand joints codes from the hand engine 1416, the head pose from the pose engine 1412, and in some cases the audio stream from the audio coder 1418.

In some cases, a spatial audio engine 1426 may receive as input the decoded audio generated by the audio decoder 1425 and in some cases the future predicted pose information from the future pose prediction engine 1438. Using the inputs to generate audio that is spatially oriented according to the pose of the user of the second client device 1404. A lip synchronization engine 1428 can synchronize the animation of the lips of the virtual representation of the user of the first client device 1402 depicted in the display 1436 with the spatial audio output by the spatial audio engine 1426.

A re-projection engine 1434 can perform re-projection to re-project the virtual content of the decoded target view frame according to the predicted pose determined by the future pose prediction engine 1438. The re-projected target view frame can then be displayed on the display 1436 so that the user of the second client device 1404 can view the virtual scene from that user's perspective.

FIG. 15 is a diagram illustrating an example of an example of an XR system 1500 configured with an avatar call flow directly between client devices, in accordance with aspects of the present disclosure. As shown in FIG. 15, the XR system 1500 includes some of the same components (with like numerals) as the XR system 1400 of FIG. 14. For example, the face encoder 1409, the geometry encoder engine 1411, the pose engine 1412, the body encoder 1414, the hand engine 1416, the audio coder 1418, the user virtual representation system 1420,which can receive user enrolled data 1450, the audio decoder 1425, the spatial audio engine 1426, the lip synchronization engine 1428, the re-projection engine 1434, the display 1436, and the future pose projection engine 1438 are configured to perform the same or similar operations as the same components of the XR system 1400 of FIG. 14.

The XR system 1500 also includes a scene composition system 1522, which may receive background scene information 1519, a video encoder 1530, and a video decoder 1532. In some cases, the user virtual representation system 1420 may output the virtual representation of the user of the first client device 1502 to a scene composition system 1522, which may be part of or implemented by a server device 1505. Background scene information 1519 and virtual representations of other users participating in the virtual session (if any exist) may also be provided to the scene composition system 1522. The background scene information 1519 can include information about the scene, such as lighting of the virtual scene, virtual objects in the scene (e.g., virtual buildings, virtual streets, virtual animals, etc.), and/or other details related to the virtual scene (e.g., a sky, clouds, etc.). In some cases, such as in an AR, MR, or VR see-through setting (where video frames of a real-world environment are displayed to a user), the lighting information can include lighting of a real-world environment in which the first client device 1502 and/or the second client device 1504 (and/or other client devices participating in the virtual session) are located. In some cases, the future predicted pose from the future pose prediction engine 1438 of the second client device 1504 may also be input to the scene composition system 1522.

Using the virtual representation of the user of the first client device 1502, the background scene information 1519, and virtual representations of other users (if any exist) (and in some cases the future predicted pose), the scene composition system 1522 can compose target view frames for the virtual scene with a view of the virtual scene from the perspective of the user of the second client device 1504 based on a relative difference between the pose of the second client device 1504 and each respective pose of the first client device 1502 and any other client devices of users participating in the virtual session. For example, a composed target view frame can include a blending of the virtual representation of the user of the first client device 1502, the background scene information 1519 (e.g., lighting, background objects, sky, etc.), and virtual representations of other users that may be involved in the virtual session. Theposes of virtual representations of any other users are also based on a pose of the second client device 1504 (corresponding to a pose of the user of the second client device 1504).

The video encoder 1530 can encode (or compress) the target view frames from the scene composition system 1522 using a video coding technique (e.g., according to any suitable video codec, such as advanced video coding (AVC), high efficiency video coding (HEVC), versatile video coding (VVC), moving picture experts group (MPEG), etc.). The video encoder 1530 may then transmit encoded target view frames to the second client device 1504 via the network.

A video decoder 1532 of the second client device 1504 may obtain the encoded target view frames and may decode the encoded target view frames using an inverse of the video coding technique performed by the video encoder 1530 (e.g., according to a video codec, such as AVC, HEVC, VVC, etc.). The re-projection engine 1434 can perform re-projection to re-project the virtual content of the decoded target view frame according to the predicted pose determined by the future pose prediction engine 1438. The re-projected target view frame can then be displayed on a display 1436 so that the user of the second client device 1504 can view the virtual scene from that user's perspective.

In some cases, for full-body pose estimation and avatar animation, a system may predict body shape/pose parameters. SMPL or ADAM body models can be used that can be parametrizable like 3DMM. Prediction may occur from images captured using cameras on an XR device (e.g., an HMD) and/or attached body sensors. The system can apply shape and pose deformation to the base model.

Aspects of 3D reconstruction can be challenging, including mouth opening, hair and facial hair, eyes, emotions, interactivity with the virtual representation or avatar, etc.

Various standards exist that are related to animation. For example, W3D has created a standard on humanoid animation, https://www.web3d.org/documents/specifications/19774-1/V2.0/index.html. The standard defines the joints of the humanoid model and their hierarchy and defines a hierarchical model of the humanoid. Humanoid components include body segments, joints, skeleton, skin with normal and coordinates, and transformations. It also defines sites where interactivity can be attached. Animations are defined in Part 2 athttps://www.web3d.org/documents/specifications/19774-2/V2.0/index.html, which supports interpolation and motion objects.

There are various problems associated with virtual representations (e.g., avatars) of users in virtual environments. For example, different applications/platforms may use different representations of virtual representations (or avatars). Further, in a shared space, virtual representations (or avatars) from all participants have to be composited into a single scene. It can be difficult to support a wide range of virtual representations (or avatars) representations in a scene description. A solution to such a problem should support a wide range of virtual representations (e.g., avatar representations), captured and synthetic avatars, animated and frame-by-frame avatars, and interactivity involving different parts of the avatar.

The systems and techniques described herein provide a way to integrate virtual representations (e.g., avatars) into a scene description. In some cases, a virtual scene may be described by a schema, such as a Graphics Language Transmission Format (glTF). The glTF may describe a virtual scene using a plurality of hierarchical tree structures describing the environment of the scene, objects in the scene, etc. In some cases, the glTF can also be used to describe virtual representations. For example, a virtual representation may include a mesh (e.g., head mesh, body mesh, etc.) onto which textures may be overlaid. In some cases, it can be useful to map segments (e.g., portions) of the mesh to humanoid components, such as body segments, that may be defined in the glTF. For example, animations and interactions may be defined based a hierarchical model of a humanoid with certain animations and/or interactions defined based on human components, such as body segments, joints, etc. For example, a glTF node may be associated with a hand, which may be mapped to a portion of the mesh and associated with an ability to touch (e.g., interact with) other objects in the environment. These interactions and/or animations may define how parts of a body mesh may be deformed, moved, warped, etc.

FIG. 16 is a block diagram illustrating an example of a virtual representation (or avatar) reconstruction system or pipeline 1600, in accordance with aspects of the present disclosure. In some cases, the virtual representation reconstruction system 1600 may be included as a part of the user virtual representation system 1420 of FIGS. 14 and 15. The virtual representation reconstruction system 1600 may include a mesh generation engine 1602, a set of buffers 1604, and a presentation engine 1606.

The mesh generation engine 1602 may include an avatar reconstruction engine 1608 which receives input information. In some cases, the input information may be received as one or more data streams or channels. For instance, the input information is received via a set of data streams including streams for a base model, which may be a generic mesh model of a virtual representation, texture information, deformation maps, parameterization data, and static metadata. The input information may be provided as input to the avatar reconstruction engine 1608.

The avatar reconstruction engine 1608 can generate components of the virtual representation, such as a vertices for the geometry of the mesh, texture information, skinning information for the textures, location of joints, interactivity information, etc., as 3D meshes. In some cases, the avatar reconstruction engine 1608 may include a set of machine learning (ML) models and/or algorithms. The components of the virtual representation may be stored in the set of buffers 1604. The set of buffers 1604 may include buffers for various types of information, such as vertex information for the mesh, texture information for the mesh, skinning/joint information for the mesh, attribute information for the mesh, interactivity and/or metadata for the mesh, etc. In some cases, the components of the virtual representation may be stored in a single combined buffer.

The output of the avatar reconstruction engine 1608 may be output from the set of buffers 1604 to the presentation engine 1606. For example, the presentation engine 1606 can receive the 3D meshes from the set of buffers 1604 and render the virtual representation. As shown, the presentation engine 1606 does not have a dependency on specific input information for the avatar reconstruction engine 1608. For instance, the presentation engine receives and processes the relevant meshes to render from the avatar reconstruction engine 1608 without respect to the input information provided to the avatar reconstruction engine 1608. The presentation engine 1606 can thus render the data in the buffer(s), allowing the specific input information for the avatar reconstruction engine 1608 to vary without affecting the presentation engine 1606.

In some aspects, the input information for the avatar reconstruction engine 1608 may vary, for example, based on a format (or type) of the virtual representation. In some cases, the format of the virtual representation may vary based on a particular vendor that is responsible for the virtual representation, a complexity of the virtual representation, a particular computingsystem being used, any combination thereof, and/or other information. For example, a virtual representation generated by a first vendor may include different input information streams which may be handled differently by the avatar reconstruction engine 1608 as compared to virtual representations generated by a second vendor. In another example, input information provided for a virtual representation from one vendor may not include specular textures, may use a different base model, may be interactive at different locations, etc., as compared to a virtual representation from another vendor. In some cases, a format of virtual representation may vary, for example, based on an account level (e.g., premium account, regular account, etc.), device being used, available bandwidth, etc.

By decoupling the presentation engine 1606 from the specific input information. different formats of virtual representations may be accommodated by adapting the avatar reconstruction engine 1608 to the different formats of virtual representations. For example, the avatar reconstruction engine 1608 may have multiple ML models, each trained to generate meshes from one or more different formats of virtual representations, or multiple avatar reconstruction engines 1608 may be used to generate meshes from one or more different formats of virtual representations. In some cases, while different input information may be received for different virtual representation formats, the input information itself may be arranged into and/or described by a common schema or format, such as gLTF.

In some cases, it may be useful to provide enhanced techniques to integrate virtual representations into such schema by extending a mesh element (e.g., a gLTF mesh element) to represent a virtual representation or a part of a virtual representation (e.g., an avatar or a part of an avatar). For example, a common schema may be defined to accommodate different formats of virtual representations. Each part of the virtual representation (or avatar) can be associated with a part of a humanoid and may be associated with certain interactivity behavior.

In some cases, a root node of the virtual representation (or avatar) can describe how the virtual representation is represented. The root node may have one or more children each associated with a humanoid part. For example, a child mesh node may indicate which humanoid part applies to that child mesh node through a path scheme (e.g., “/humanoid/arms/left/hand”).

FIG. 17 is a diagram illustrating a structure of a virtual representation (or avatar) in glTF 1700, in accordance with aspects of the present disclosure. FIG. 17 includes a root node 1702 (e.g., parent node), under which child nodes representing the virtual representation arehierarchically arranged. In some cases, this hierarchical arrangement may be based on humanoid components, such as body segments such as a body (e.g., torso), arms, hands, fingers, legs, etc. with smaller sub-segments, such as fingers, represented by child nodes (e.g., sub-nodes) of larger components, such as hands. In some cases, the body segments (and sub-segments) and hierarchy for the nodes may be defined based on a hierarchical model of a humanoid, such as that provided in the W3D standard.

In some cases, a virtual representation framework that is used to generate a virtual representation (e.g., for generating a virtual representation in a particular virtual representation format) specifies how information is arranged in the input information and how to map segments (or processed segments) of the input information into a node structure of a glTF schema. For example, to allow different virtual representation formats to be used with a common presentation engine, a structure for interpreting the different virtual representation formats may be provided. As an example of such a structure, the root node 1702 may include one or more fields 1704 which help allow different virtual representation formats to be accommodated. The fields 1704 may include a type field 1706, a mappings field 1708, and a sources field 1710. While the root node 1702 is illustrated in FIG. 17 as a first node (e.g., trunk node, root node, primary node, etc.) for the virtual representation, the root node 1702 may be a child node for the glTF representing a scene. Further, while the one or more fields 1704 are described as a part of the root node 1702, it should be understood that the one or more fields 1704 may be included in any defined segment of the node structure for the virtual representation. For example, the one or more fields 1704 may be included in a specific child node of the node structure.

In some cases, the type field 1706 may be used to indicate a format for the virtual representation (e.g., the virtual representation framework used to generate the virtual representation). As an example, the type field may include a uniform resource name (URN) or uniform resource locator (URL) indicating the format for the virtual representation. The URN/URL may provide an indication of the format for the virtual representation, which may be used to determine how (e.g., which algorithm) to use for reconstructing the virtual representation.

The mappings field 1708 may indicate one or more child nodes (e.g., sub-nodes) under the root node 1702 that correspond to various body segments. For example, thepresentation engine 1606 may use the mappings field 1708 to determine where in the glTF to find information about certain body segments which have interactivity. The mappings field 1708 may indicate that, for example, information about the right hand is in a particular node of the node hierarchy and that the node is associated with interactivity information that may be in another node of the node hierarchy. In some cases, the interactivity information of a node/sub-node may include information about whether and/or how the body segment associated with the node/sub-node can interact with other objects and/or the environment.

The sources field 1710 may indicate where certain information may be located in the input information. For example, the input information may be received in one or more data streams, and the sources field 1710 may include information indicating where in the data streams certain information is located. As a more specific example, the sources field 1710 may indicate that body pose information may be available in a certain segment of a deformation map data stream.

Upon reconstruction of the virtual representation (or avatar) from its representation, the mesh data can be mapped to the signaled mesh structure. The system can map interactivity metadata into the reconstructed virtual representation (or avatar) mesh.

The scene description can include a description of the 3D reconstructed virtual representation (or avatar) as a dynamic mesh and/or as a skinned mesh. The system or pipeline of FIG. 17 may take different representations of the virtual representation (or avatar) and perform the 3D reconstruction. The reconstructed avatar components may then be fed into the presentation engine of FIG. 16 for rendering. Examples of inputs into the system of FIG. 16 can include basic meshes, one or more different types of textures, color images (e.g., red-green-blue (RGB) images) and/or depth images, deformation parameters, skinning weights, any combination thereof, and/or other inputs.

FIG. 18 is an example of a JavaScript Object Notation (JSON) schema 1800 for the systems and techniques described herein. In the example schema 1800, the type field includes a URN indicating a format for the virtual representation. The mappings field includes an array of paths into the node hierarchy of the nodes representing the virtual representation and the paths correspond the humanoid parts to child nodes (e.g., sub-nodes) in the node hierarchy.The sources field include an array of pointers (e.g., links) to where information for the virtual representation may be located in the input information data streams.

FIG. 19 is a flow diagram illustrating a process 1900 for generating virtual content in a distributed system, in accordance with aspects of the present disclosure. The process 1900 may be performed by a computing device (or apparatus) or a component (e.g., a chipset, codec, etc.) of the computing device, such as CPU 510 and/or GPU 525 of FIG. 5, and/or processor 2012 of FIG. 20. The computing device may be an animation and scene rendering system (e.g., an edge or cloud-based server, a personal computer acting as a server device, a mobile device such as a mobile phone acting as a server device, an XR device acting as a server device, a network router, or other device acting as a server or other device). The operations of the process 1900 may be implemented as software components that are executed and run on one or more processors (e.g., CPU 510 and/or GPU 525 of FIG. 5, and/or processor 2012 of FIG. 20).

At block 1902, the computing device (or component thereof) may obtain information describing a virtual representation of a user (e.g., glTF 1700 of FIG. 17), the information including a hierarchical set of nodes. A first node of the hierarchical set of nodes includes a root node for the hierarchical set of nodes. The first node includes a mapping configuration (e.g., mapping field 1708 of FIG. 17) for mapping child nodes of the hierarchical set of nodes to segments of the virtual representation of the user. A child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user. In some cases, the segment includes at least one body part of the virtual representation of the user. In some cases, the segment of the virtual representation of the user comprises a body part of the virtual representation of the user. In some cases, the body part of the virtual representation of the user comprises a humanoid component. In some cases, the first node comprises a root node (e.g., root node 1702 of FIG. 17) for the hierarchical set of nodes. In some examples, the first node includes type information, and the portion of the data associated with the child node is processed based on the type information (e.g., type field 1706 of FIG. 17). In some cases, the type information comprises information indication how the virtual representation of the user is represented. In some examples, the type information comprises a universal resource name indicating the format for the virtual representation of the user. In some cases, the type information may be used to indicate a format for the virtual representation of the user (e.g., the virtual representation framework used to generate the virtual representation of the user). Insome examples, the computing device (or component thereof) may process the portion of the data associated with the child node by processing the portion of the data associated with the child node based on the indicated format for the virtual representation of the user. In some cases, the first node includes source information, and wherein the portion of the data associated with the child node is identified based on source information. In some examples, the data comprises one or more data streams, and wherein the one or more data streams is based on the format for the virtual representation of the user. For example, the daya may be received via a set of data streams including streams for a base model, which may be a generic mesh model of a virtual representation of the user, texture information, deformation maps, parameterization data, and static metadata. In some cases, a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user. In some examples, the mappings may indicate one or more child nodes (e.g., sub-nodes) under the root node that correspond to various body segments.

At block 1904, the computing device (or component thereof) may identify a portion of the data associated with the child node. In some cases, the sources information may indicate where certain information may be located in the input information. In some cases, the portion of the data associated with the child node includes interactivity information indicating whether the segment of the virtual representation of the user can interact with other objects.

At block 1906, the computing device (or component thereof) may process the portion of the data associated with the child node to generate a segment of the virtual representation of the user. In some cases, the generated segment of the virtual representation of the user comprises mesh information. In some cases, the computing device (or component thereof) may process the mesh information to render the generated segment of the virtual representation of the user.

The systems and techniques described herein provide interactivity with virtual representations (or avatars). For example, the systems and techniques provide a mapping scheme between the input avatar components and the output avatar. The systems and techniques also provide a standardized naming scheme for scene nodes that map to avatar segments (e.g., thumb, right/left hand, etc.). The interactivity can be assigned to avatars using the standardized naming scheme, such as /humanoid/arms/left/fingers/index triggers a light on when in proximity of light switch. The mapping may use one of the input components, such as a basehumanoid or unposed geometry parts, texture coordinates in a texture map (e.g., patch (x,y,w,h) maps to left hand index finger), a combination thereof, or other inputs.

As described above, the systems and techniques decouple a virtual representation (e.g., an avatar representation) from the virtual representation integration in the scene description. The systems and techniques allow referencing different types of avatar representations and mapping the reconstructed 3D avatar onto humanoid parts that can be associated with interactivity behaviors.

In some cases, the computing devices or apparatuses configured to perform the operations of one or more of the processes described herein may include a processor, microprocessor, microcomputer, or other component of a device that is configured to carry out the steps of the process. In some examples, such devices or apparatuses may include one or more sensors configured to capture image data and/or other sensor measurements. In some examples, such computing device or apparatus may include one or more sensors and/or a camera configured to capture one or more images or videos. In some cases, such device or apparatus may include a display for displaying images. In some examples, the one or more sensors and/or camera are separate from the device or apparatus, in which case the device or apparatus receives the sensed data. Such device or apparatus may further include a network interface configured to communicate data.

The components of the device or apparatus configured to carry out one or more operations of the processes described herein can be implemented in circuitry. For example, the components can include and/or can be implemented using electronic circuits or other electronic hardware, which can include one or more programmable electronic circuits (e.g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), and/or other suitable electronic circuits), and/or can include and/or be implemented using computer software, firmware, or any combination thereof, to perform the various operations described herein. The computing device may further include a display (as an example of the output device or in addition to the output device), a network interface configured to communicate and/or receive the data, any combination thereof, and/or other component(s). The network interface may be configured to communicate and/or receive Internet Protocol (IP) based data or other type of data.

The operations of the various processes can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes.

Additionally, the processes described herein may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program including a plurality of instructions executable by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.

FIG. 20 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. In particular, FIG. 20 illustrates an example of computing system 2000, which can be for example any computing device making up internal computing system, a remote computing system, a camera, or any component thereof in which the components of the system are in communication with each other using connection 2005. Connection 2005 can be a physical connection using a bus, or a direct connection into processor 2010, such as in a chipset architecture. Connection 2005 can also be a virtual connection, networked connection, or logical connection.

In some aspects, computing system 2000 is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some aspects, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some aspects, the components can be physical or virtual devices.

Example system 2000 includes at least one processing unit (CPU or processor) 2010 and connection 2005 that couples various system components including system memory 2015, such as read-only memory (ROM) 2020 and random-access memory (RAM) 2025 to processor 2010. Computing system 2000 can include a cache 2011 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 2010.

Processor 2010 can include any general-purpose processor and a hardware service or software service, such as services 2032, 2034, and 2036 stored in storage device 2030, configured to control processor 2010 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 2010 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction, computing system 2000 includes an input device 2045, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 2000 can also include output device 2035, which can be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 2000. Computing system 2000 can include communications interface 2040, which can generally govern and manage the user input and system output.

The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications using wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, WLAN signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signaltransfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/long term evolution (LTE) cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof.

The communications interface 2040 may also include one or more GNSS receivers or transceivers that are used to determine a location of the computing system 2000 based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 2030 can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a Europay, Mastercard and Visa (EMV) chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, RAM, static RAM (SRAM), dynamic RAM (DRAM), ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L#), resistive random-access memory (RRAM/RcRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof.

The storage device 2030 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 2010, it causes the system to perform a function. In some aspects, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 2010, connection 2005, output device 2035, etc., to carry out the function. The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections.

The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-readable medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information. data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like.

In some aspects, the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Specific details are provided in the description above to provide a thorough understanding of the aspects and examples provided herein. However, it will be understood by one of ordinary skill in the art that the aspects may be practiced without these specific details. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the aspects in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the aspects.

Individual aspects may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general-purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing processes and methods according to these disclosures can include hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Typical examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.

In the foregoing description, aspects of the application are described with reference to specific aspects thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative aspects of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, aspects can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate aspects, the methods may be performed in a different order than that described.

One of ordinary skill will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology used herein can be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description.

Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.

The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly.

Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or

B and C. A and B and C, or any duplicate information or data (e.g., A and A, B and B, C and C. A and A and B, and so on), or any other ordering, duplication, or combination of A, B, and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” or “at least one of A or B” may mean A, B, or A and B, and may additionally include items not listed in the set of A and B. The phrases “at least one” and “one or more” are used interchangeably hercin.

Claim language or other language reciting “at least one processor configured to,” “at least one processor being configured to,” “one or more processors configured to,” “one or more processors being configured to,” or the like indicates that one processor or multiple processors (in any combination) can perform the associated operation(s). For example, claim language reciting “at least one processor configured to: X, Y, and Z” means a single processor can be used to perform operations X, Y, and Z; or that multiple processors are each tasked with a certain subset of operations X, Y, and Z such that together the multiple processors perform X, Y, and Z; or that a group of multiple processors work together to perform operations X, Y, and Z. In another example, claim language reciting “at least one processor configured to: X, Y, andZ” can mean that any single processor may only perform at least a subset of operations X, Y, and Z.

Where reference is made to one or more elements performing functions (e.g., steps of a method), one element may perform all functions, or more than one element may collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and/or each function need not be performed in whole by only one element (e.g., different elements may perform different sub-functions of a function). Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to cause the other element to perform the functions.

Where reference is made to an entity (e.g., any entity or device described herein) performing functions or being configured to perform functions (e.g., steps of a method), the entity may be configured to cause one or more elements (individually or collectively) to perform the functions. The one or more components of the entity may include at least one memory, at least one processor, at least one communication interface, another component configured to perform one or more (or all) of the functions, and/or any combination thereof. Where reference to the entity performing functions, the entity may be configured to cause one component to perform all functions, or to cause more than one component to collectively perform the functions. When the entity is configured to cause more than one component to collectively perform the functions, each function need not be performed by each of those components (e.g., different functions may be performed by different components) and/or each function need not be performed in whole by only one component (e.g., different components may perform different sub-functions of a function).

The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the examples disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium including program code including instructions that, when executed, performs one or more of the methods, algorithms, and/or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may include memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically crasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves.

The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and amicroprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.

Illustrative aspects of the disclosure include:

Aspect 1. A method for generating a virtual representation of a user, comprising: receiving data describing a virtual representation of the user, the data including a hierarchical set of nodes, wherein a first node of the set of nodes includes type information, source information, a mapping, or a combination thereof, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identifying, based on the type information, a format associated with the virtual representation of the user; identifying, based on the mapping, the child node in the hierarchical set of nodes; identifying, based on the source information, a segment of the data associated with the child node; and processing the data associated with the segment of the data associated with the child node based on a corresponding format for the virtual representation of the user to generate a segment of the virtual representation of the user.

Aspect 2. The method of Aspect 1, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 3. The method of any one of Aspects 1 or 2, wherein the segment of the virtual representation comprises a body part of the virtual representation of the user.

Aspect 4. The method of Aspect 3, wherein the body part of the virtual representation of the user comprises a humanoid component.

Aspect 5. The method of any one of Aspects 1 to 4, wherein the segment of the data associated with the child node includes interactivity information.

Aspect 6. The method of any one of Aspects 1 to 5, wherein the first node comprises a root node for the hierarchical set of nodes.

Aspect 7. The method of any one of Aspects 1 to 6, wherein the type information comprises a universal resource name indicating the format for the virtual representation of the user.

Aspect 8. The method of any one of Aspects 1 to 7, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 9. The method of Aspect 8, further comprising processing the mesh information to render a segment of the virtual representation of the user.

Aspect 10. The method of any one of Aspects 1 to 9, wherein the data comprises one or more data streams, and wherein the one or more data streams may vary based on the format for the virtual representation of the user.

Aspect 11. An apparatus for generating a virtual representation of a user, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: receive data describing a virtual representation of the user, the data including a hierarchical set of nodes, wherein a first node of the set of nodes includes type information, source information, a mapping, or a combination thereof, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identify, based on the type information, a format associated with the virtual representation of the user; identify, based on the mapping, the child node in the hierarchical set of nodes; identify, based on the source information, a segment of the data associated with the child node; and process the data associated with the segment of the data associated with the child node based on a corresponding format for the virtual representation of the user to generate a segment of the virtual representation of the user.

Aspect 12. The apparatus of Aspect 11, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 13. The apparatus of any one of Aspects 11 or 12, wherein the segment of the virtual representation of the user comprises a body part of the virtual representation of the user.

Aspect 14. The apparatus of Aspect 13, wherein the body part of the virtual representation of the user comprises a humanoid component.

Aspect 15. The apparatus of any one of Aspects 11 to 14, wherein the segment of the data associated with the child node includes interactivity information.

Aspect 16. The apparatus of any one of Aspects 11 to 15, wherein the first node comprises a root node for the hierarchical set of nodes.

Aspect 17. The apparatus of any one of Aspects 11 to 16, wherein the type information comprises a universal resource name indicating the format for the virtual representation of the user.

Aspect 18. The apparatus of any one of Aspects 11 to 17, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 19. The apparatus of Aspect 18, wherein the at least one processor is further configured to process the mesh information to render a segment of the virtual representation of the user.

Aspect 20. The apparatus of any one of Aspects 11 to 19, wherein the data comprises one or more data streams, and wherein the one or more data streams may vary based on the format for the virtual representation of the user.

Aspect 21. A non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to: receive data describing a virtual representation of a user, the data including a hierarchical set of nodes, wherein a first node of the set of nodes includes type information, source information, a mapping, or a combination thereof, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identify, based on the type information, a format associated with the virtual representation of the user; identify, based on the mapping, the child node in the hierarchical set of nodes; identify, based on the source information, a segment of the data associated with the child node; and process the data associated with the segment of the data associated with the child node based on a corresponding format for the virtual representation of the user to generate a segment of the virtual representation of the user.

Aspect 22. The non-transitory computer-readable medium of Aspect 21, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 23. The non-transitory computer-readable medium of any one of Aspects 21 or 22, wherein the segment of the virtual representation of the user comprises a body part of the virtual representation of the user.

Aspect 24. The non-transitory computer-readable medium of Aspect 23, wherein the body part of the virtual representation of the user comprises a humanoid component.

Aspect 25. The non-transitory computer-readable medium of any one of Aspects 21 to 24, wherein the segment of the data associated with the child node includes interactivity information.

Aspect 26. The non-transitory computer-readable medium of any one of Aspects 21 to 25, wherein the first node comprises a root node for the hierarchical set of nodes.

Aspect 27. The non-transitory computer-readable medium of any one of Aspects 21 to 26, wherein the type information comprises a universal resource name indicating the format for the virtual representation of the user.

Aspect 28. The non-transitory computer-readable medium of any one of Aspects 21 to 27, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 29. The non-transitory computer-readable medium of Aspect 28, wherein the instructions cause the at least one processor to process the mesh information to render a segment of the virtual representation of the user.

Aspect 30. The non-transitory computer-readable medium of any one of Aspects 21 to 29, wherein the data comprises one or more data streams, and wherein the one or more data streams may vary based on the format for the virtual representation of the user.

Aspect 31. An apparatus for generating a virtual representation of a user, the apparatus including one or more means for performing operations according to any of Aspects 1 to 10.

Aspect 41. A method for generating a virtual representation of a user, comprising: obtaining information describing a virtual representation of the user, the information including a hierarchical set of nodes, wherein a first node of the hierarchical set of nodes comprises a root node for the hierarchical set of nodes, wherein the first node includes a mapping configuration for mapping child nodes of the hierarchical set of nodes to segments of the virtual representation of the user, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identifying a portion of the data associated with the child node; and processing the portion of the data associated with the child node to generate the segment of the virtual representation of the user.

Aspect 42. The method of Aspect 41, wherein the segment includes at least one body part of the virtual representation of the user.

Aspect 43. The method of Aspect 42, wherein the at least one body part of the virtual representation of the user comprises a humanoid component.

Aspect 44. The method of any of Aspects 41-43, wherein the first node includes type information, and further wherein the portion of the data associated with the child node is processed based on the type information.

Aspect 45. The method of Aspect 44, wherein the type information comprises information indication how the virtual representation of the user is represented.

Aspect 46. The method of any of Aspects 44-45, wherein the type information comprises a universal resource name indicating a format for the virtual representation of the user.

Aspect 47. The method of Aspect 46, wherein processing the portion of the data associated with the child node comprises processing the portion of the data associated with the child node based on the indicated format for the virtual representation of the user.

Aspect 48. The method of any of Aspects 41-47, wherein the first node includes source information, and wherein the portion of the data associated with the child node is identified based on source information.

Aspect 49. The method of any of Aspects 41-48, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 50. The method of any of Aspects 41-49, wherein the portion of the data associated with the child node includes interactivity information indicating whether the segment of the virtual representation of the user can interact with other objects.

Aspect 51. The method of any of Aspects 41-50, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 52. The method of Aspect 51, further comprising processing the mesh information to render the generated segment of the virtual representation of the user.

Aspect 53. The method of any of Aspects 41-52, wherein the data comprises one or more data streams, and wherein the one or more data streams is based on a format for the virtual representation of the user.

Aspect 54. An apparatus for generating a virtual representation of a user, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: obtain information describing a virtual representation of the user, the information including a hierarchical set of nodes, wherein a first node of the hierarchical set of nodes comprises a root node for the hierarchical set of nodes, wherein the first node includes a mapping configuration for mapping child nodes of the hierarchical set of nodes to segments of the virtual representation of the user, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identify a portion of the data associated with the child node; and process the portion of the data associated with the child node to generate the segment of the virtual representation of the user.

Aspect 55. The apparatus of Aspect 54, wherein the segment includes at least one body part of the virtual representation of the user.

Aspect 56. The apparatus of Aspect 55, wherein the at least one body part of the virtual representation of the user comprises a humanoid component.

Aspect 57. The apparatus of any of Aspects 54-56, wherein the first node includes type information, and further wherein the portion of the data associated with the child node is processed based on the type information.

Aspect 58. The apparatus of Aspect 57, wherein the type information comprises information indication how the virtual representation of the user is represented.

Aspect 59. The apparatus of any of Aspects 57-58, wherein the type information comprises a universal resource name indicating a format for the virtual representation of the user.

Aspect 60. The apparatus of Aspect 59, wherein, to process the portion of the data associated with the child node, the at least one processor is configured to process the portion of the data associated with the child node based on the indicated format for the virtual representation of the user.

Aspect 61. The apparatus of any of Aspects 54-60, wherein the first node includes source information, and wherein the portion of the data associated with the child node is identified based on source information.

Aspect 62. The apparatus of any of Aspects 54-61, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 63. The apparatus of any of Aspects 54-62, wherein the portion of the data associated with the child node includes interactivity information indicating whether the segment of the virtual representation of the user can interact with other objects.

Aspect 64. The apparatus of any of Aspects 54-63, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 65. The apparatus of Aspect 64, wherein the at least one processor is further configured to process the mesh information to render the generated segment of the virtual representation of the user.

Aspect 66. The apparatus of Aspect 64-68, wherein the data comprises one or more data streams, and wherein the one or more data streams is based on a format for the virtual representation of the user.

Aspect 67. A non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to: obtain information describing a virtual representation of a user, the information including a hierarchical set of nodes, wherein a first node of the hierarchical set of nodes comprises a root node for the hierarchical set of nodes, wherein the first node includes a mapping configuration for mapping child nodes of the hierarchical set of nodes to segments of the virtual representation of the user, and wherein a child node of the hierarchical set of nodes includes data associated with a segment of the virtual representation of the user; identify a portion of the data associated with the child node; and process the portion of the data associated with the child node to generate the segment of the virtual representation of the user.

Aspect 68. The non-transitory computer-readable medium of Aspect 67, wherein the segment includes at least one body part of the virtual representation of the user.

Aspect 69. The non-transitory computer-readable medium of Aspect 68, wherein the at least one body part of the virtual representation of the user comprises a humanoid component.

Aspect 70. The non-transitory computer-readable medium of Aspect 67-69, wherein the first node includes type information, and further wherein the portion of the data associated with the child node is processed based on the type information.

Aspect 71. The non-transitory computer-readable medium of Aspect 70, wherein the type information comprises information indication how the virtual representation of the user is represented.

Aspect 72. The non-transitory computer-readable medium of any of Aspects 70-71, wherein the type information comprises a universal resource name indicating a format for the virtual representation of the user.

Aspect 73. The non-transitory computer-readable medium of Aspect 72, wherein, to process the portion of the data associated with the child node, the instructions cause the at leastone processor to process the portion of the data associated with the child node based on the indicated format for the virtual representation of the user.

Aspect 74. The non-transitory computer-readable medium of any of Aspects 67-73, wherein the first node includes source information, and wherein the portion of the data associated with the child node is identified based on source information.

Aspect 75. The non-transitory computer-readable medium of any of Aspects 67-74, wherein a sub-node of the child node includes data associated with a sub-segment of the segment of the virtual representation of the user.

Aspect 76. The non-transitory computer-readable medium of any of Aspects 67-75, wherein the portion of the data associated with the child node includes interactivity information indicating whether the segment of the virtual representation of the user can interact with other objects.

Aspect 77. The non-transitory computer-readable medium of any of Aspects 67-76, wherein the generated segment of the virtual representation of the user comprises mesh information.

Aspect 78. The non-transitory computer-readable medium of any of Aspects 67-77, wherein the instructions cause the at least one processor to process the mesh information to render the generated segment of the virtual representation of the user.

Aspect 79. The non-transitory computer-readable medium of any of Aspects 67-78, wherein the data comprises one or more data streams, and wherein the one or more data streams is based on a format for the virtual representation of the user.

Aspect 80. An apparatus for generating a virtual representation of a user, the apparatus including one or more means for performing operations according to any of Aspects 41-53.