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公开(公告)号：US10936909B2
公开(公告)日：2021-03-02
申请号：US16188130
申请日：2018-11-12
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Sunil Hadap , Jonathan Eisenmann , Jinsong Zhang , Emiliano Gambaretto
Abstract: Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate lighting parameters for input images where the input images are synthetic and real low-dynamic range images. Such a neural network system can be trained using differences between a simple scene rendered using the estimated lighting parameters and the same simple scene rendered using known ground-truth lighting parameters. Such a neural network system can also be trained such that the synthetic and real low-dynamic range images are mapped in roughly the same distribution. Such a trained neural network system can be used to input a low-dynamic range image determine high-dynamic range lighting parameters.
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公开(公告)号：US20200302684A1
公开(公告)日：2020-09-24
申请号：US16877227
申请日：2020-05-18
Applicant: ADOBE INC.
Inventor： Kalyan Sunkavalli , Sunil Hadap , Nathan Carr , Mathieu Garon
Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that use a local-lighting-estimation-neural network to estimate lighting parameters for specific positions within a digital scene for augmented reality. For example, based on a request to render a virtual object in a digital scene, a system uses a local-lighting-estimation-neural network to generate location-specific-lighting parameters for a designated position within the digital scene. In certain implementations, the system also renders a modified digital scene comprising the virtual object at the designated position according to the parameters. In some embodiments, the system generates such location-specific-lighting parameters to spatially vary and adapt lighting conditions for different positions within a digital scene. As requests to render a virtual object come in real (or near real) time, the system can quickly generate different location-specific-lighting parameters that accurately reflect lighting conditions at different positions within a digital scene in response to render requests.
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公开(公告)号：US10692276B2
公开(公告)日：2020-06-23
申请号：US15970367
申请日：2018-05-03
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Zexiang Xu , Sunil Hadap
Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.
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公开(公告)号：US20200074682A1
公开(公告)日：2020-03-05
申请号：US16675641
申请日：2019-11-06
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Matthew David Fisher , Jonathan Eisenmann , Emiliano Gambaretto
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.
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公开(公告)号：US10515460B2
公开(公告)日：2019-12-24
申请号：US15826331
申请日：2017-11-29
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Matthew David Fisher , Jonathan Eisenmann , Emiliano Gambaretto
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.
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Patent Agency Ranking
公开(公告)号：US20200273237A1
公开(公告)日：2020-08-27
申请号：US15930925
申请日：2020-05-13
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Zexiang Xu , Sunil Hadap
Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.
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公开(公告)号：US10692265B2
公开(公告)日：2020-06-23
申请号：US16676733
申请日：2019-11-07
Applicant: Adobe Inc.
Inventor： Sunil Hadap , Elya Shechtman , Zhixin Shu , Kalyan Sunkavalli , Mehmet Yumer
Abstract: Techniques are disclosed for performing manipulation of facial images using an artificial neural network. A facial rendering and generation network and method learns one or more compact, meaningful manifolds of facial appearance, by disentanglement of a facial image into intrinsic facial properties, and enables facial edits by traversing paths of such manifold(s). The facial rendering and generation network is able to handle a much wider range of manipulations including changes to, for example, viewpoint, lighting, expression, and even higher-level attributes like facial hair and age—aspects that cannot be represented using previous models.
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公开(公告)号：US20200090389A1
公开(公告)日：2020-03-19
申请号：US16676733
申请日：2019-11-07
Applicant: Adobe Inc.
Inventor： Sunil Hadap , Elya Shechtman , Zhixin Shu , Kalyan Sunkavalli , Mehmet Yumer
Abstract: Techniques are disclosed for performing manipulation of facial images using an artificial neural network. A facial rendering and generation network and method learns one or more compact, meaningful manifolds of facial appearance, by disentanglement of a facial image into intrinsic facial properties, and enables facial edits by traversing paths of such manifold(s). The facial rendering and generation network is able to handle a much wider range of manipulations including changes to, for example, viewpoint, lighting, expression, and even higher-level attributes like facial hair and age—aspects that cannot be represented using previous models.
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公开(公告)号：US20190164312A1
公开(公告)日：2019-05-30
申请号：US15826331
申请日：2017-11-29
Applicant: ADOBE INC.
Inventor： Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Matthew David Fisher , Jonathan Eisenmann , Emiliano Gambaretto
CPC classification number: G06T7/80 , G06N3/0454 , G06N3/08 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.
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公开(公告)号：US11257284B2
公开(公告)日：2022-02-22
申请号：US15930925
申请日：2020-05-13
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Zexiang Xu , Sunil Hadap
Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.
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