Texas Instruments Corporation

Qualcomm Incorporated

Ajit Deepak Gupte

Pushkar Gorur Sheshagiri

Chiranjib Choudhuri

Pawan Kumar Baheti

Ajit Venkat Rao

Mahesh Madhukar Mehendale

Sandeep Kanakapura Lakshmikantha

Hetul Sanghvi

Ranga Ramanujam Srinivasan

Madhukar Budagavi

Gerhard Reitmayr

Chayan Sharma

Anupama S

Avani Rao

Ning Bi

Michel Adib Sarkis

Vinay Melkote Krishnaprasad

Amitabh Menon

Mina Ayman Saleh Yanni Makar

Ke-Li Cheng

Sudipto Banerjee

Jais Abraham

Chieh-Ming Kuo

Kuang-Man Huang

Abhay Golecha

Srujan Babu Nandipati

Naveen Srinivasamurthy

Alexander Tessarolo

Venkatesh Natarajan

Shilpi Sahu

Gregory Raymond Shurtz

Sanjeev Kumar

Abhijeet Bisain

Ashish Bajaj

Mel Alan Phipps

Mullangi Venkata Ratna Reddy

Hemanth Acharya

Tom Edward Botterill

Subash Chandar Govindarajan

Herve Catan

Padmapriya Jagannathan

Vinod Kumar Saini

Aditya Bhuvanagiri

Rajeshwar Kurapaty

Aravind Korlepara

Arindam Basak

R V Jagannadha Rao Doddi

Aakash Agrawal

Shankaranarayana Karantha Deshamangala

Amit Brahme

Youngmin Park

Navin Acharya

Systems and techniques are provided for generating a three-dimensional (3D) facial model. For example, a process can include obtaining at least one input image associated with a face. In some aspects, the process can include obtaining a pose for a 3D facial model associated with the face. In some examples, the process can include generating, by a machine learning model, the 3D facial model associated with the face. In some cases, one or more parameters associated with a shape component of the 3D facial model are conditioned on the pose. In some implementations, the 3D facial model is configured to vary in shape based on the pose for the 3D facial model associated with the face.

View dependent three-dimensional morphable models

The architecture shown can perform global search, local search and local sub pixel search in a parallel or in a pipelined mode. All operations are in a streaming mode without the requirement of external intermediate data storage.

Apparatus and method for efficient motion estimation

Systems and techniques are described herein for using head mounted display (HMD) hinge motion to calibrate the head mounted device. For example, an device (or component of the device) can obtain, via one or more sensors, sensor measurements during rotation of a display of the HMD between a first end of a range of motion of a hinge of the display about an axis and a second end of the range of motion of the hinge about the axis; determine parameters of the HMD based on the sensor measurements and the rotation of the display between the first end of the range of motion of the hinge and the second end of the range of motion of the hinge about the axis; and determine pose information for the HMD based on the determined parameters.

Head mounted display (HMD) device calibration based on hinge motion

Systems and techniques are provided for generating a texture for a three-dimensional (3D) facial model. For example, a process can include obtaining a first frame, the first frame including a first portion of a face. In some aspects, the process can include generating a 3D facial model based on the first frame and generating a first facial feature corresponding to the first portion of the face. In some examples, the process includes obtaining a second frame, the second frame including a second portion of the face. In some cases, the second portion of the face at least partially overlaps the first portion of the face. In some examples, the process includes combining the first facial feature with the second facial feature to generate an enhanced facial feature, wherein the combining is performed to enhance an appearance of select areas of the enhanced facial feature.

Facial texture synthesis for three-dimensional morphable models

Systems and techniques are described herein for processing frames. The systems and techniques can be implemented by various types of systems, such as by an extended reality (XR) system or device. In some cases, a process can include obtaining feature information associated with a feature in a current frame, wherein the feature information is based on one or more previous frames; determining an estimated pose of the apparatus associated with the current frame; obtaining a distance associated with the feature in the current frame; and determining an estimated scale of the feature in the current frame based on the feature information associated with the feature, the estimated pose, and the distance associated with the feature.

Keypoint detection and feature descriptor computation

Several methods and systems for chroma residual data prediction for encoding blocks corresponding to video data are disclosed. In an embodiment, at least one coefficient correlating reconstructed luma residual samples and corresponding reconstructed chroma residual samples is computed for one or more encoded blocks of video data. Predicted chroma residual samples are generated for encoding a block of video data based on corresponding reconstructed luma residual samples and the at least one coefficient.

Methods and systems for chroma residual data prediction

Systems and techniques are provided for performing user authentication. For example, a process can include obtaining a plurality of images associated with a face and a facial expression of the user, wherein each respective image of the plurality of images includes a different portion of the face. An encoder neural network can be used to generate one or more predicted three-dimensional (3D) facial modeling parameters, wherein the encoder neural network generates the one or more predicted 3D facial modeling parameters based on the plurality of images. A reference 3D facial model associated with the face and the facial expression can be obtained. An error can be determined between the one or more predicted 3D facial modeling parameters and the reference 3D facial model, and the user can be authenticated user based on the error being less than a pre-determined authentication threshold.

User authentication based on three-dimensional face modeling using partial face images

Methods, devices, and apparatuses are provided to facilitate a positioning of an item of virtual content in an extended reality environment. For example, a placement position for an item of virtual content can be transmitted to one or more of a first device and a second device. The placement position can be based on correlated map data generated based on first map data obtained from the first device and second map data obtained from the second device. In some examples, the first device can transmit the placement position to the second device.

Content positioning in extended reality systems

Methods of encoding a video stream in a video encoder and decoding an encoded video stream in a video decoder using a low complexity large transform are provided. An encoding method includes receiving an n×n residual block in a transform component of the video encoder, and transforming the n×n residual block using an n×n transform to generate an n×n transform coefficient block, wherein the n×n transform is based on (n/m*n/m) m×m Hadamard transforms and (m*m) (n/m)×(n/m) discrete cosine transforms, wherein m<n. A decoding method includes receiving an n×n transform coefficient block in an inverse transform component of the video decoder, and applying an n×n inverse transform to the n×n transform coefficient block to reconstruct an n×n residual block, wherein the n×n inverse transform is based on (n/m*n/m) m×m Hadamard transforms and (m*m) (n/m)×(n/m) inverse discrete cosine transforms, wherein m<n.

Low complexity large transform

Systems and techniques are described herein for processing images. The systems and techniques can be implemented by various types of systems, such as by an extended reality (XR) system or device. In some cases, a first processor receives an image of an environment captured by an image sensor, identifies features depicted in the image, and generates descriptors for the features. The first processor sends the descriptors to a second processor, which may be more powerful than the first processor. The second processor receives the descriptors. The second processor associates the plurality of features with a map of the environment based on at least a subset of the plurality of descriptors. For example, the second processor can track at least a subset of the features based on at least a subset of the descriptors and based on feature information from one or more additional images of the environment.

Feature processing in extended reality systems

Systems and techniques are provided for determining and applying corrected poses in digital content experiences. An example method can include receiving, from one or more sensors associated with an apparatus, inertial measurements and one or more frames of a scene; based on the one or more frames and the inertial measurements, determining, via a first filter, an angular and linear motion of the apparatus and a gravity vector indicating a direction of gravitational force interacting with the apparatus; when a motion of the apparatus is below a threshold, determining, via a second filter, an updated gravity vector indicating a direction of gravitational force interacting with the apparatus; determining, based on the updated gravity vector, parameters for aligning an axis of the scene with a gravity direction in a real-world spatial frame; and aligning, using the parameters, the axis of the scene with the gravity direction in the real-world spatial frame.

Pose correction for digital content

Methods of encoding a video stream in a video encoder and decoding an encoded video stream in a video decoder using a low complexity large transform are provided. An encoding method includes receiving an n&#xd7;n residual block in a transform component of the video encoder, and transforming the n&#xd7;n residual block using an n&#xd7;n transform to generate an n&#xd7;n transform coefficient block, wherein the n&#xd7;n transform is based on (n/m*n/m) m&#xd7;m Hadamard transforms and (m*m) (n/m)&#xd7;(n/m) discrete cosign transforms, wherein m&#x3c;n. A decoding method includes receiving an n&#xd7;n transform coefficient block in an inverse transform component of the video decoder, and applying an n&#xd7;n inverse transform to the n&#xd7;n transform coefficient block to reconstruct an n&#xd7;n residual block, wherein the n&#xd7;n inverse transform is based on (n/m*n/m) m&#xd7;m Hadamard transforms and (m*m) (n/m)&#xd7;(n/m) inverse discrete cosign transforms, wherein m&#x3c;n.

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Karnataka, India

Ajit Deepak Gupte