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公开(公告)号：US12093830B2
公开(公告)日：2024-09-17
申请号：US16976805
申请日：2019-07-23
Applicant: Google LLC
Inventor： Shahram Izadi , Cem Keskin
IPC: G06N3/084 , G06F18/213 , G06N3/048 , G06N3/08
CPC classification number: G06N3/084 , G06F18/213 , G06N3/048 , G06N3/08
Abstract: Methods, systems, and apparatus for more efficiently and accurately generating neural network outputs, for instance, for use in classifying image or audio data. In one aspect, a method includes processing a network input using a neural network including multiple neural network layers to generate a network output. One or more of the neural network layers is a conditional neural network layer. Processing a layer input using a conditional neural network layer to generate a layer output includes obtaining values of one or more decision parameters of the conditional neural network layer. The neural network processes the layer input and the decision parameters of the conditional neural network layer to determine values of one or more latent parameters of the conditional neural network layer from a continuous set of possible latent parameter values. The values of the latent parameters specify the values of the conditional layer weights.
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公开(公告)号：US11030773B2
公开(公告)日：2021-06-08
申请号：US16798881
申请日：2020-02-24
Applicant: Google LLC
Inventor： Jonathan James Taylor , Vladimir Tankovich , Danhang Tang , Cem Keskin , Adarsh Prakash Murthy Kowdle , Philip L. Davidson , Shahram Izadi , David Kim
Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.
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公开(公告)号：US20200349772A1
公开(公告)日：2020-11-05
申请号：US16861530
申请日：2020-04-29
Applicant: Google LLC
Inventor： Anastasia Tkach , Ricardo Martin Brualla , Shahram Izadi , Shuoran Yang , Cem Keskin , Sean Ryan Francesco Fanello , Philip Davidson , Jonathan Taylor , Rohit Pandey , Andrea Tagliasacchi , Pavlo Pidlypenskyi
Abstract: A method includes receiving a first image including color data and depth data, determining a viewpoint associated with an augmented reality (AR) and/or virtual reality (VR) display displaying a second image, receiving at least one calibration image including an object in the first image, the object being in a different pose as compared to a pose of the object in the first image, and generating the second image based on the first image, the viewpoint and the at least one calibration image.
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公开(公告)号：US11995899B2
公开(公告)日：2024-05-28
申请号：US17302291
申请日：2021-04-29
Applicant: Google LLC
Inventor： Qinge Wu , Grant Yoshida , Catherine Boulanger , Erik Hubert Dolly Goossens , Cem Keskin , Sofien Bouaziz , Jonathan James Taylor , Nidhi Rathi , Seth Raphael
CPC classification number: G06V20/63 , G02B27/0093 , G02B27/017 , G06V10/255 , G02B2027/0138 , G02B2027/014 , G06V30/10
Abstract: A head-mounted device (HMD) can be configured to determine a request for recognizing at least one content item included within content framed within a display of the HMD. The HMD can be configured to initiate a head-tracking process that maintains a coordinate system with respect to the content, and a pointer-tracking process that tracks a pointer that is visible together with the content within the display. The HMD can be configured to capture a first image of the content and a second image of the content, the second image including the pointer. The HMD can be configured to map a location of the pointer within the second image to a corresponding image location within the first image, using the coordinate system, and provide the at least one content item from the corresponding image location.
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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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Patent Agency Ranking
公开(公告)号：US20220350997A1
公开(公告)日：2022-11-03
申请号：US17302291
申请日：2021-04-29
Applicant: Google LLC
Inventor： Qinge Wu , Grant Yoshida , Catherine Boulanger , Erik Hubert Dolly Goossens , Cem Keskin , Sofien Bouaziz , Jonathan James Taylor , Nidhi Rathi , Seth Raphael
Abstract: A head-mounted device (HMD) can be configured to determine a request for recognizing at least one content item included within content framed within a display of the HMD. The HMD can be configured to initiate a head-tracking process that maintains a coordinate system with respect to the content, and a pointer-tracking process that tracks a pointer that is visible together with the content within the display. The HMD can be configured to capture a first image of the content and a second image of the content, the second image including the pointer. The HMD can be configured to map a location of the pointer within the second image to a corresponding image location within the first image, using the coordinate system, and provide the at least one content item from the corresponding image location.
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公开(公告)号：US11328486B2
公开(公告)日：2022-05-10
申请号：US16861530
申请日：2020-04-29
Applicant: Google LLC
Inventor： Anastasia Tkach , Ricardo Martin Brualla , Shahram Izadi , Shuoran Yang , Cem Keskin , Sean Ryan Francesco Fanello , Philip Davidson , Jonathan Taylor , Rohit Pandey , Andrea Tagliasacchi , Pavlo Pidlypenskyi
Abstract: A method includes receiving a first image including color data and depth data, determining a viewpoint associated with an augmented reality (AR) and/or virtual reality (VR) display displaying a second image, receiving at least one calibration image including an object in the first image, the object being in a different pose as compared to a pose of the object in the first image, and generating the second image based on the first image, the viewpoint and the at least one calibration image.
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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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9.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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10.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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