Google Inc.

Danhang Tang

Shahram Izadi

Jonathan James Taylor

Philip L Davidson

Cem Keskin

Adarsh Prakash Murthy Kowdle

David Kim

Mingsong Dou

Sean Ryan Francesco Fanello

Vladimir Tankovich

Sofien Bouaziz

Ruofei Du

Rohit Kumar Pandey

Christoph Rhemann

Yinda Zhang

Onur Gonen Guleryuz

Paul E Debevec

Alex Olwal

Saurabh Singh

Graham Fyffe

Sean Ryan Fanello

Sameh Khamis

Andrea Tagliasacchi

Christian Haene

Julien Pascal Christophe Valentin

Emily B Cooper

Xueming Yu

Jessica Lynn Busch

Jason Angelo Dourgarian

Sergio Orts Escolano

Peter Christopher Lincoln

Deqing Sun

Geoffrey Douglas Harvey

Phillip Andrew Chou

Kaiwen Guo

Mathieu Simon Le Goc

Feitong Tan

Systems and methods are directed to encoding and/or decoding of the textures/geometry of a three-dimensional volumetric representation. An encoding computing system can obtain voxel blocks from a three-dimensional volumetric representation of an object. The encoding computing system can encode voxel blocks with a machine-learned voxel encoding model to obtain encoded voxel blocks. The encoding computing system can decode the encoded voxel blocks with a machine-learned voxel decoding model to obtain reconstructed voxel blocks. The encoding computing system can generate a reconstructed mesh representation of the object based at least in part on the one or more reconstructed voxel blocks. The encoding computing system can encode textures associated with the voxel blocks according to an encoding scheme and based at least in part on the reconstructed mesh representation of the object to obtain encoded textures.

Systems and methods for compression of three-dimensional volumetric representations

A lighting stage includes a plurality of lights that project alternating spherical color gradient illumination patterns onto an object or human performer at a predetermined frequency. The lighting stage also includes a plurality of cameras that capture images of an object or human performer corresponding to the alternating spherical color gradient illumination patterns. The lighting stage also includes a plurality of depth sensors that capture depth maps of the object or human performer at the predetermined frequency. The lighting stage also includes (or is associated with) one or more processors that implement a machine learning algorithm to produce a three-dimensional (3D) model of the object or human performer. The 3D model includes relighting parameters used to relight the 3D model under different lighting conditions.

Volumetric performance capture with relighting

Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.

Systems and methods for training models to predict dense correspondences in images using geodesic distances

Systems and methods are provided in which physical objects in the ambient environment can function as user interface implements in an augmented reality environment. A physical object detected within a field of view of a camera of a computing device may be designated as a user interface implement in response to a user command. User interfaces may be attached to the designated physical object, to provide a tangible user interface implement for user interaction with the augmented reality environment.

Tangible six-degree-of-freedom interfaces for augmented reality

An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

Hand tracking based on articulated distance field

An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch 'cube'). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.

System and method for active stereo depth sensing

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Mountain View, CA, United States of America

Danhang Tang