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公开(公告)号：US12260572B2
公开(公告)日：2025-03-25
申请号：US17907529
申请日：2021-08-05
Applicant: Google LLC
Inventor： Varun Jampani , Huiwen Chang , Kyle Sargent , Abhishek Kar , Richard Tucker , Dominik Kaeser , Brian L. Curless , David Salesin , William T. Freeman , Michael Krainin , Ce Liu
Abstract: A method includes determining, based on an image having an initial viewpoint, a depth image, and determining a foreground visibility map including visibility values that are inversely proportional to a depth gradient of the depth image. The method also includes determining, based on the depth image, a background disocclusion mask indicating a likelihood that pixel of the image will be disoccluded by a viewpoint adjustment. The method additionally includes generating, based on the image, the depth image, and the background disocclusion mask, an inpainted image and an inpainted depth image. The method further includes generating, based on the depth image and the inpainted depth image, respectively, a first three-dimensional (3D) representation of the image and a second 3D representation of the inpainted image, and generating a modified image having an adjusted viewpoint by combining the first and second 3D representation based on the foreground visibility map.
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公开(公告)号：US12249178B2
公开(公告)日：2025-03-11
申请号：US17745158
申请日：2022-05-16
Applicant: Google LLC
Inventor： Forrester H. Cole , Dilip Krishnan , William T. Freeman , David Benjamin Belanger
Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.
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公开(公告)号：US20240249422A1
公开(公告)日：2024-07-25
申请号：US17907529
申请日：2021-08-05
Applicant: Google LLC
Inventor： Varun Jampani , Huiwen Chang , Kyle Sargent , Abhishek Kar , Richard Tucker , Dominik Kaeser , Brian L. Curless , David Salesin , William T. Freeman , Michael Krainin , Ce Liu
CPC classification number: G06T7/50 , G06T5/60 , G06T5/77 , G06T2207/20081
Abstract: A method includes determining, based on an image having an initial viewpoint, a depth image, and determining a foreground visibility map including visibility values that are inversely proportional to a depth gradient of the depth image. The method also includes determining, based on the depth image, a background disocclusion mask indicating a likelihood that pixel of the image will be disoccluded by a viewpoint adjustment. The method additionally includes generating, based on the image, the depth image, and the background disocclusion mask, an inpainted image and an inpainted depth image. The method further includes generating, based on the depth image and the inpainted depth image, respectively, a first three-dimensional (3D) representation of the image and a second 3D representation of the inpainted image, and generating a modified image having an adjusted viewpoint by combining the first and second 3D representation based on the foreground visibility map.
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公开(公告)号：US20230206955A1
公开(公告)日：2023-06-29
申请号：US17927101
申请日：2020-05-22
Applicant: Google LLC
Inventor： Forrester H. Cole , Erika Lu , Tali Dekel , William T. Freeman , David Henry Salesin , Michael Rubinstein
IPC: G11B27/00 , G06V10/82 , G06V20/40 , G11B27/031
CPC classification number: G11B27/005 , G06V10/82 , G06V20/46 , G11B27/031
Abstract: A computer-implemented method for decomposing videos into multiple layers (212, 213) that can be re-combined with modified relative timings includes obtaining video data including a plurality of image frames (201) depicting one or more objects. For each of the plurality of frames, the computer-implemented method includes generating one or more object maps descriptive of a respective location of at least one object of the one or more objects within the image frame. For each of the plurality of frames, the computer-implemented method includes inputting the image frame and the one or more object maps into a machine-learned layer Tenderer model. (220) For each of the plurality of frames, the computer-implemented method includes receiving, as output from the machine-learned layer Tenderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps. The object layers include image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings.
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公开(公告)号：US20190095698A1
公开(公告)日：2019-03-28
申请号：US16061344
申请日：2017-09-27
Applicant: Google LLC
Inventor： Forrester H. Cole , Dilip Krishnan , William T. Freeman , David Benjamin Belanger
Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.
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Patent Agency Ranking
公开(公告)号：US12243145B2
公开(公告)日：2025-03-04
申请号：US17927101
申请日：2020-05-22
Applicant: Google LLC
Inventor： Forrester H. Cole , Erika Lu , Tali Dekel , William T. Freeman , David Henry Salesin , Michael Rubinstein
Abstract: A computer-implemented method for decomposing videos into multiple layers (212, 213) that can be re-combined with modified relative timings includes obtaining video data including a plurality of image frames (201) depicting one or more objects. For each of the plurality of frames, the computer-implemented method includes generating one or more object maps descriptive of a respective location of at least one object of the one or more objects within the image frame. For each of the plurality of frames, the computer-implemented method includes inputting the image frame and the one or more object maps into a machine-learned layer Tenderer model. (220) For each of the plurality of frames, the computer-implemented method includes receiving, as output from the machine-learned layer Tenderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps. The object layers include image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings.
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公开(公告)号：US10650227B2
公开(公告)日：2020-05-12
申请号：US16061344
申请日：2017-09-27
Applicant: Google LLC
Inventor： Forrester H. Cole , Dilip Krishnan , William T. Freeman , David Benjamin Belanger
Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.
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公开(公告)号：US12249030B2
公开(公告)日：2025-03-11
申请号：US17922160
申请日：2020-04-30
Applicant: Google LLC
Inventor： Cristian Sminchisescu , Hongyi Xu , Eduard Gabriel Bazavan , Andrei Zanfir , William T. Freeman , Rahul Sukthankar
IPC: G06T17/20 , G06N3/0455 , G06N3/08 , G06T19/20
Abstract: The present disclosure provides a statistical, articulated 3D human shape modeling pipeline within a fully trainable, modular, deep learning framework. In particular, aspects of the present disclosure are directed to a machine-learned 3D human shape model with at least facial and body shape components that are jointly trained end-to-end on a set of training data. Joint training of the model components (e.g., including both facial, hands, and rest of body components) enables improved consistency of synthesis between the generated face and body shapes.
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公开(公告)号：US20220270402A1
公开(公告)日：2022-08-25
申请号：US17745158
申请日：2022-05-16
Applicant: Google LLC
Inventor： Forrester H. Cole , Dilip Krishnan , William T. Freeman , David Benjamin Belanger
Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.
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公开(公告)号：US11335120B2
公开(公告)日：2022-05-17
申请号：US16857219
申请日：2020-04-24
Applicant: Google LLC
Inventor： Forrester H. Cole , Dilip Krishnan , William T. Freeman , David Benjamin Belanger
Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.
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