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公开(公告)号：US20220292314A1
公开(公告)日：2022-09-15
申请号：US17200643
申请日：2021-03-12
Applicant: Google LLC
Inventor： Cristian Sminchisescu , Andrei Zanfir , Eduard Gabriel Bazavan , Mihai Zanfir , William Tafel Freeman , Rahul Sukthankar
Abstract: Systems and methods are directed to a method for estimation of an object state from image data. The method can include obtaining two-dimensional image data depicting an object. The method can include processing, with an estimation portion of a machine-learned object state estimation model, the two-dimensional image data to obtain an initial estimated state of the object. The method can include, for each of one or more refinement iterations, obtaining a previous loss value associated with a previous estimated state for the object, processing the previous loss value to obtain a current estimated state of the object, and evaluating a loss function to determine a loss value associated with the current estimated state of the object. The method can include providing a final estimated state for the object.
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公开(公告)号：US11908071B2
公开(公告)日：2024-02-20
申请号：US17495960
申请日：2021-10-07
Applicant: Google LLC
Inventor： Cristian Sminchisescu , Mihai Zanfir , Andrei Zanfir , Eduard Gabriel Bazavan , William Tafel Freeman , Rahul Sukthankar
CPC classification number: G06T17/00 , G06N3/08 , G06N20/00 , G06T11/003 , G06T2207/20081
Abstract: The present disclosure is generally directed to reconstructing representations of bodies from images. An example method of the present disclosure includes inputting, into a machine-learned reconstruction model, input data descriptive of an image depicting a body; predicting, using a machine-learned marker prediction component of the reconstruction model, a set of surface marker locations on the body; and outputting, using a machine-learned marker poser component of the reconstruction model, an output representation of the body that corresponds to the set of surface marker locations. In the example method, one or more parameters of the reconstruction model were learned at least in part based on a consistency loss corresponding to a distance between relaxed-constraint representations generated from a prior set of surface marker locations predicted according to the one or more parameters and parametric representations generated from the prior set using kinematic constraints associated with the body.
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Patent Agency Ranking
公开(公告)号：US20240249523A1
公开(公告)日：2024-07-25
申请号：US18560609
申请日：2022-05-11
Applicant: Google LLC
Inventor： Forrester H. Cole , Andrew Zisserman , Tali Dekel , William Tafel Freeman , Erika Lu , Michael Rubinstein
CPC classification number: G06V20/46 , G06T7/194 , G06T7/246 , G06T7/73 , G06V10/26 , G06V10/776 , G06V10/82 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure provides systems and methods for identifying and extracting object-related effects in videos. Given an ordinary video and a rough segmentation mask overtime of one or more subjects of interest, example systems proposed herein can estimate an omnimatte for each subject—an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Example implementations of the proposed models can be trained only on the input video in a self-supervised manner, without any manual labels, and are generic. For example, the models can produce omnimattes automatically for arbitrary objects and a variety of effects.
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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：US20250166136A1
公开(公告)日：2025-05-22
申请号：US18957367
申请日：2024-11-22
Applicant: Google LLC
Inventor： Mark Jeffrey Matthews , Prafull Sharma , Dmitry Lagun , Xuhui Jia , Yuanzhen Li , Varun Jampani , William Tafel Freeman
Abstract: Provided are systems and methods for controlling material attributes such as roughness, metallic, albedo, and transparency in real images. This method leverages the generative prior of text-to-image models known for their photorealistic capabilities, offering an alternative to traditional rendering pipelines. As one example, the technology can be used to alter the appearance of an object in an image, making it appear more metallic or changing its roughness to create a more matte or glossy finish. This can be particularly useful in various fields where the ability to manipulate the appearance of products in images can be a powerful tool.
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公开(公告)号：US11836221B2
公开(公告)日：2023-12-05
申请号：US17200643
申请日：2021-03-12
Applicant: Google LLC
Inventor： Cristian Sminchisescu , Andrei Zanfir , Eduard Gabriel Bazavan , Mihai Zanfir , William Tafel Freeman , Rahul Sukthankar
CPC classification number: G06F18/217 , G06N3/08 , G06T7/73 , G06V40/103 , G06T2207/20081 , G06T2207/20084 , G06T2207/30196
Abstract: Systems and methods are directed to a method for estimation of an object state from image data. The method can include obtaining two-dimensional image data depicting an object. The method can include processing, with an estimation portion of a machine-learned object state estimation model, the two-dimensional image data to obtain an initial estimated state of the object. The method can include, for each of one or more refinement iterations, obtaining a previous loss value associated with a previous estimated state for the object, processing the previous loss value to obtain a current estimated state of the object, and evaluating a loss function to determine a loss value associated with the current estimated state of the object. The method can include providing a final estimated state for the object.
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公开(公告)号：US20230116884A1
公开(公告)日：2023-04-13
申请号：US17495960
申请日：2021-10-07
Applicant: Google LLC
Inventor： Cristian Sminchisescu , Mihai Zanfir , Andrei Zanfir , Eduard Gabriel Bazavan , William Tafel Freeman , Rahul Sukthankar
Abstract: The present disclosure is generally directed to reconstructing representations of bodies from images. An example method of the present disclosure includes inputting, into a machine-learned reconstruction model, input data descriptive of an image depicting a body; predicting, using a machine-learned marker prediction component of the reconstruction model, a set of surface marker locations on the body; and outputting, using a machine-learned marker poser component of the reconstruction model, an output representation of the body that corresponds to the set of surface marker locations. In the example method, one or more parameters of the reconstruction model were learned at least in part based on a consistency loss corresponding to a distance between relaxed-constraint representations generated from a prior set of surface marker locations predicted according to the one or more parameters and parametric representations generated from the prior set using kinematic constraints associated with the body.
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