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1.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：US11790550B2
公开(公告)日：2023-10-17
申请号：US17292647
申请日：2020-07-08
Applicant: Google LLC
Inventor： Taihong Xiao , Deqing Sun , Ming-Hsuan Yang , Qifei Wang , Jinwei Yuan
CPC classification number: G06T7/593 , G06T7/215 , G06T2207/10012 , G06T2207/20081
Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.
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公开(公告)号：US20250166135A1
公开(公告)日：2025-05-22
申请号：US18951203
申请日：2024-11-18
Applicant: Google LLC
Inventor： Yu-Chuan Su , Hsin-Ping Huang , Ming-Hsuan Yang , Deqing Sun , Lu Jiang , Yukun Zhu , Xuhui Jia
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controllable video generation. One of the methods includes receiving a text prompt that specifies an object; receiving a control input that comprises an image that depicts a particular instance of the object; generating a video that comprises a respective video frame at each of a plurality of time steps in the video and that depicts the particular instance of the object. Generating the video includes, at each of the plurality of time steps: obtaining a text prompt embedding; obtaining a control input embedding; and generating the respective video frame at the time step using a video generation neural network while the video generation neural network is conditioned on the text prompt embedding and on the control input embedding.
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Patent Agency Ranking
公开(公告)号：US20240013497A1
公开(公告)日：2024-01-11
申请号：US18252118
申请日：2020-12-21
Applicant: Google LLC
Inventor： Deqing Sun , Varun Jampani , Gengshan Yang , Daniel Vlasic , Huiwen Chang , Forrester H. Cole , Ce Liu , William Tafel Freeman
CPC classification number: G06T19/20 , G06T7/55 , G06T17/20 , G06T7/20 , G06T7/40 , G06T2207/30244 , G06T2207/10016 , G06T2207/20084 , G06T2219/2021 , G06T2207/20081
Abstract: A computing system and method can be used to render a 3D shape from one or more images. In particular, the present disclosure provides a general pipeline for learning articulated shape reconstruction from images (LASR). The pipeline can reconstruct rigid or nonrigid 3D shapes. In particular, the pipeline can automatically decompose non-rigidly deforming shapes into rigid motions near rigid-bones. This pipeline incorporates an analysis-by-synthesis strategy and forward-renders silhouette, optical flow, and color images which can be compared against the video observations to adjust the internal parameters of the model. By inverting a rendering pipeline and incorporating optical flow, the pipeline can recover a mesh of a 3D model from the one or more images input by a user.
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公开(公告)号：US20240265490A1
公开(公告)日：2024-08-08
申请号：US18436509
申请日：2024-02-08
Applicant: Google LLC
Inventor： Janne Matias Kontkanen , Eric Tabellion , Brian Lee Curless , Fitsum Reda , Deqing Sun , Caroline Rebecca Pantofaru
IPC: G06T3/4007 , G06T3/18 , G06T7/246
CPC classification number: G06T3/4007 , G06T3/18 , G06T7/248 , G06T2207/20081
Abstract: Provided is a computer system that includes one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned image interpolation model. The machine-learned image interpolation model is configured to: extract, for each of multiple different scales, a respective set of feature values from each of a pair of input images; generate, for each of the multiple different scales, a respective flow estimate for each of the pair of input images that indicates a respective flow from the interpolation time to the respective capture time; warp, for each of the multiple different scales, the respective set of feature values for each of the pair of input images according to the respective flow estimate to generate respective warped sets of features; and generate a interpolated image based on the respective warped sets of features for the pair of input images and the multiple different scales.
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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：US20220189051A1
公开(公告)日：2022-06-16
申请号：US17292647
申请日：2020-07-08
Applicant: Google LLC
Inventor： Taihong Xiao , Deqing Sun , Ming-Hsuan Yang , Qifei Wang , Jinwei Yuan
Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.
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