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公开(公告)号：US20200257891A1
公开(公告)日：2020-08-13
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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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：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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Patent Agency Ranking
公开(公告)号：US20230083892A1
公开(公告)日：2023-03-16
申请号：US17798024
申请日：2021-02-08
Applicant: Google LLC
Inventor： David Benjamin Belanger , Georgiana Andreea Gane , Christof Angermueller , David W. Sculley, II , David Martin Dohan , Kevin Patrick Murphy , Lucy Colwell , Zelda Elaine Mariet
Abstract: Methods and systems for performing black box optimization to identify an output that optimizes an objective.
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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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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：US20220172055A1
公开(公告)日：2022-06-02
申请号：US17601105
申请日：2020-04-10
Applicant: Google LLC
Inventor： Maxwell Bileschi , Lucy Colwell , Theodore Sanderson , David Benjamin Belanger , Jamie Alexander Smith , Drew Bryant , Mark Andrew DePristo , Brandon Carter
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting biological functions of proteins. In one aspect, a method comprises: obtaining data defining a sequence of amino acids in a protein; processing the data defining the sequence of amino acids in the protein using a neural network, wherein: the neural network is a convolutional neural network comprising one or more dilated convolutional layers; and the neural network is configured to process the data defining the sequence of amino acids in the protein in accordance with trained parameter values of the neural network to generate a neural network output characterizing at least one predicted biological function of the sequence of amino acids in the protein; and identifying the predicted biological function of the sequence of amino acids in the protein using the neural network output.
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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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