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公开(公告)号：US20230154051A1
公开(公告)日：2023-05-18
申请号：US17919460
申请日：2020-04-17
Applicant: Google LLC
Inventor： Danhang Tang , Saurabh Singh , Cem Keskin , Phillip Andrew Chou , Christian Haene , Mingsong Dou , Sean Ryan Francesco Fanello , Jonathan Taylor , Andrea Tagliasacchi , Philip Lindsley Davidson , Yinda Zhang , Onur Gonen Guleryuz , Shahram Izadi , Sofien Bouaziz
IPC: G06T9/00
Abstract: 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.
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公开(公告)号：US20250045968A1
公开(公告)日：2025-02-06
申请号：US18570562
申请日：2021-06-16
Applicant: Google LLC
Inventor： Onur G. Guleryuz , Ruofei Du , Hugues H. Hoppe , Sean Ryan Francesco Fanello , Philip Andrew Chou , Danhang Tang , Philip Davidson
Abstract: Nonlinear peri-codec optimization for image and video coding includes obtaining a source image including pixel values expressed in a first defined image sample space, generating a neuralized image representing the source image, the neuralized image including pixel values that are expressed as neural latent space values, encoding the input image wherein the neural latent space values are used as pixel values in a second defined image sample space and the input image is in an operative image format of the encoder, such that a decoder decodes the encoded image to obtain a reconstructed image in the second defined image sample space, wherein the reconstructed image is a reconstructed neuralized image including reconstructed neural latent space values, such that a deneuralized reconstructed image corresponding to the source image is obtained by a nonlinear post-codec image processor in the first defined image sample space.
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公开(公告)号：US20240290025A1
公开(公告)日：2024-08-29
申请号：US18588948
申请日：2024-02-27
Applicant: GOOGLE LLC
Inventor： Yinda Zhang , Sean Ryan Francesco Fanello , Ziqian Bai , Feitong Tan , Zeng Huang , Kripasindhu Sarkar , Danhang Tang , Di Qiu , Abhimitra Meka , Ruofei Du , Mingsong Dou , Sergio Orts Escolano , Rohit Kumar Pandey , Thabo Beeler
CPC classification number: G06T13/40 , G06T7/90 , G06T17/20 , G06V10/44 , G06T2207/10024 , G06T2207/20084
Abstract: A method comprises receiving a first sequence of images of a portion of a user, the first sequence of images being monocular images; generating an avatar based on the first sequence of images, the avatar being based on a model including a feature vector associated with a vertex; receiving a second sequence of images of the portion of the user; and based on the second sequence of images, modifying the avatar with a displacement of the vertex to represent a gesture of the avatar.
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4.发明公开公开(公告)号：US20240212325A1
公开(公告)日：2024-06-27
申请号：US18596822
申请日：2024-03-06
Applicant: Google LLC
Inventor： Yinda Zhang , Feitong Tan , Danhang Tang , Mingsong Dou , Kaiwen Guo , Sean Ryan Francesco Fanello , Sofien Bouaziz , Cem Keskin , Ruofei Du , Rohit Kumar Pandey , Deqing Sun
IPC: G06V10/771 , G06T7/70 , G06T17/00 , G06V10/44 , G06V10/75
CPC classification number: G06V10/771 , G06T7/70 , G06T17/00 , G06V10/44 , G06V10/751 , G06T2207/20081 , G06T2207/20084
Abstract: 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.
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公开(公告)号：US20240303908A1
公开(公告)日：2024-09-12
申请号：US18547628
申请日：2021-04-30
Applicant: GOOGLE LLC
Inventor： Yinda Zhang , Danhang Tang , Ruofei Du , Zhang Chen , Kyle Genova , Sofien Bouaziz , Thomas Allen Funkhouser , Sean Ryan Francesco Fanello , Christian Haene
Abstract: A method including generating a first vector based on a first grid and a three-dimensional (3D) position associated with a first implicit representation (IR) of a 3D object, generating at least one second vector based on at least one second grid and an upsampled first grid, decoding the first vector to generate a second IR of the 3D object, decoding the at least one second vector to generate at least one third IR of the 3D object, generating a composite IR of the 3D object based on the second IR of the 3D object and the at least one third IR of the 3D object, and generating a reconstructed volume representing the 3D object based on the composite IR of the 3D object.
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Patent Agency Ranking
公开(公告)号：US11954899B2
公开(公告)日：2024-04-09
申请号：US18274371
申请日：2021-03-11
Applicant: Google LLC
Inventor： Yinda Zhang , Feitong Tan , Danhang Tang , Mingsong Dou , Kaiwen Guo , Sean Ryan Francesco Fanello , Sofien Bouaziz , Cem Keskin , Ruofei Du , Rohit Kumar Pandey , Deqing Sun
IPC: G06V10/771 , G06T7/70 , G06T17/00 , G06V10/44 , G06V10/75
CPC classification number: G06V10/771 , G06T7/70 , G06T17/00 , G06V10/44 , G06V10/751 , G06T2207/20081 , G06T2207/20084
Abstract: 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.
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7.发明公开公开(公告)号：US20240046618A1
公开(公告)日：2024-02-08
申请号：US18274371
申请日：2021-03-11
Applicant: Google LLC
Inventor： Yinda Zhang , Feitong Tan , Danhang Tang , Mingsong Dou , Kaiwen Guo , Sean Ryan Francesco Fanello , Sofien Bouaziz , Cem Keskin , Ruofei Du , Rohit Kumar Pandey , Deqing Sun
IPC: G06V10/771 , G06T17/00 , G06T7/70 , G06V10/44 , G06V10/75
CPC classification number: G06V10/771 , G06T17/00 , G06T7/70 , G06V10/44 , G06V10/751 , G06T2207/20081 , G06T2207/20084
Abstract: 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.
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公开(公告)号：US20240212184A1
公开(公告)日：2024-06-27
申请号：US18555059
申请日：2021-04-30
Applicant: Google LLC
Inventor： Ruofei Du , David Li , Danhang Tang , Yinda Zhang
CPC classification number: G06T7/55 , G06T5/77 , G06T7/181 , G06T15/00 , G06T17/20 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221
Abstract: A method including predicting a stereo depth associated with a first panoramic image and a second panoramic image, the first panoramic image and the second panoramic image being captured with a time interlude between the capture of the first panoramic image and the second panoramic image, generating a first mesh representation based on the first panoramic image and a stereo depth corresponding to the first panoramic image, generating a second mesh representation based on the second panoramic image and a stereo depth corresponding to the second panoramic image, and synthesizing a third panoramic image based on fusing the first mesh representation with the second mesh representation.
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公开(公告)号：US20230305672A1
公开(公告)日：2023-09-28
申请号：US17656818
申请日：2022-03-28
Applicant: Google LLC
Inventor： Ruofei Du , Alex Olwal , Mathieu Simon Le Goc , David Kim , Danhang Tang
IPC: G06F3/04815 , G10L15/22 , G02B27/01
CPC classification number: G06F3/04815 , G10L15/22 , G02B27/0172 , G02B27/0176 , G02B2027/0178 , G10L2015/223
Abstract: 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.
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公开(公告)号：US12066282B2
公开(公告)日：2024-08-20
申请号：US17413847
申请日：2020-11-11
Applicant: GOOGLE LLC
Inventor： Sean Ryan Francesco Fanello , Kaiwen Guo , Peter Christopher Lincoln , Philip Lindsley Davidson , Jessica L. Busch , Xueming Yu , Geoffrey Harvey , Sergio Orts Escolano , Rohit Kumar Pandey , Jason Dourgarian , Danhang Tang , Adarsh Prakash Murthy Kowdle , Emily B. Cooper , Mingsong Dou , Graham Fyffe , Christoph Rhemann , Jonathan James Taylor , Shahram Izadi , Paul Ernest Debevec
IPC: G01B11/25 , G01B11/245 , G06T15/50 , G06T17/20
CPC classification number: G01B11/2513 , G01B11/245 , G06T15/506 , G06T17/205
Abstract: 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.
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