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公开(公告)号：US20240362427A1
公开(公告)日：2024-10-31
申请号：US18308907
申请日：2023-04-28
Applicant: Adobe Inc.
Inventor： Mukul Gupta , Yaman Kumar , Rahul Gupta , Prerna Bothra , Mayur Hemani , Mayank Gupta , Gaurav Makkar
IPC: G06F40/56 , G06F40/106 , G06F40/169
CPC classification number: G06F40/56 , G06F40/106 , G06F40/169
Abstract: In implementations of systems for generating digital content, a computing device implements a generation system to receive a user input specifying a characteristic for digital content. The generation system generates input text based on the characteristic for processing by a first machine learning model. Output text generated by the first machine learning model based on processing the input text is received. The output text describes a digital content component. The generation system generates the digital content component by processing the output text using a second machine learning model. The generation system generates the digital content including the digital content component for display in a user interface based on the characteristic.
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公开(公告)号：US11645786B2
公开(公告)日：2023-05-09
申请号：US17654529
申请日：2022-03-11
Applicant: Adobe Inc.
Inventor： Meet Patel , Mayur Hemani , Karanjeet Singh , Amit Gupta , Apoorva Gupta , Balaji Krishnamurthy
CPC classification number: G06T9/002 , G06N3/08 , G06T7/0002 , G06T2207/20081 , G06T2207/20084 , G06T2207/20224
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.
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公开(公告)号：US11335033B2
公开(公告)日：2022-05-17
申请号：US17032704
申请日：2020-09-25
Applicant: Adobe Inc.
Inventor： Meet Patel , Mayur Hemani , Karanjeet Singh , Amit Gupta , Apoorva Gupta , Balaji Krishnamurthy
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.
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公开(公告)号：US20220101564A1
公开(公告)日：2022-03-31
申请号：US17032704
申请日：2020-09-25
Applicant: Adobe Inc.
Inventor： Meet Patel , Mayur Hemani , Karanjeet Singh , Amit Gupta , Apoorva Gupta , Balaji Krishnamurthy
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.
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15.发明申请公开(公告)号：US20210142539A1
公开(公告)日：2021-05-13
申请号：US16679165
申请日：2019-11-09
Applicant: Adobe Inc.
Inventor： Kumar Ayush , Surgan Jandial , Abhijeet Kumar , Mayur Hemani , Balaji Krishnamurthy , Ayush Chopra
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.
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Patent Agency Ranking
公开(公告)号：US10347052B2
公开(公告)日：2019-07-09
申请号：US14944658
申请日：2015-11-18
Applicant: ADOBE INC.
Inventor： Mayur Hemani , Naveen Prakash Goel , Kedar Vijay Bodas , Amit Mittal
IPC: G06T19/20
Abstract: Local color information in a 3D mesh is used to enhance fine geometric features such as those in embroidered clothes for 3D printing. In some implementations, vertex color information is used to detect edges and to enhance geometry. In one embodiment, a 3D model is projected into a 2D space to obtain a 2D image, so that pixels that lie on edges in the 2D image can be detected. Further, such edge information is propagated back to the 3D model to enhance the geometry of the 3D model. Other embodiments may be described and/or claimed.
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公开(公告)号：US20250005824A1
公开(公告)日：2025-01-02
申请号：US18341982
申请日：2023-06-27
Applicant: ADOBE INC.
Inventor： Rishabh Jain , Mayur Hemani , Duygu Ceylan Aksit , Krishna Kumar Singh , Jingwan Lu , Mausoom Sarkar , Balaji Krishnamurthy
Abstract: Systems and methods for image processing are described. One aspect of the systems and methods includes receiving a plurality of images comprising a first image depicting a first body part and a second image depicting a second body part and encoding, using a texture encoder, the first image and the second image to obtain a first texture embedding and a second texture embedding, respectively. Then, a composite image is generated using a generative decoder, the composite image depicting the first body part and the second body part based on the first texture embedding and the second texture embedding.
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公开(公告)号：US20250005812A1
公开(公告)日：2025-01-02
申请号：US18215484
申请日：2023-06-28
Applicant: Adobe Inc.
Inventor： Rishabh Jain , Mayur Hemani , Mausoom Sarkar , Krishna Kumar Singh , Jingwan Lu , Duygu Ceylan Aksit , Balaji Krishnamurthy
Abstract: In implementations of systems for human reposing based on multiple input views, a computing device implements a reposing system to receive input data describing: input digital images; pluralities of keypoints corresponding to the input digital images, the pluralities of keypoints representing poses of a person depicted in the input digital images; and a plurality of keypoints representing a target pose. The reposing system generates selection masks corresponding to the input digital images by processing the input data using a machine learning model. The selection masks represent likelihoods of spatial correspondence between pixels of an output digital image and portions of the input digital images. The reposing system generates the output digital image depicting the person in the target pose for display in a user interface based on the selection masks and the input data.
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公开(公告)号：US20240428564A1
公开(公告)日：2024-12-26
申请号：US18213118
申请日：2023-06-22
Applicant: Adobe Inc.
Inventor： Rishabh Jain , Mayur Hemani , Mausoom Sarkar , Krishna Kumar Singh , Jingwan Lu , Duygu Ceylan Aksit , Balaji Krishnamurthy
Abstract: In implementations of systems for generating images for human reposing, a computing device implements a reposing system to receive input data describing an input digital image depicting a person in a first pose, a first plurality of keypoints representing the first pose, and a second plurality of keypoints representing a second pose. The reposing system generates a mapping by processing the input data using a first machine learning model. The mapping indicates a plurality of first portions of the person in the second pose that are visible in the input digital image and a plurality of second portions of the person in the second pose that are invisible in the input digital image. The reposing system generates an output digital image depicting the person in the second pose by processing the mapping, the first plurality of keypoints, and the second plurality of keypoints using a second machine learning model.
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公开(公告)号：US11861772B2
公开(公告)日：2024-01-02
申请号：US17678237
申请日：2022-02-23
Applicant: Adobe Inc.
Inventor： Ayush Chopra , Rishabh Jain , Mayur Hemani , Balaji Krishnamurthy
Abstract: In implementations of systems for generating images for virtual try-on and pose transfer, a computing device implements a generator system to receive input data describing a first digital image that depicts a person in a pose and a second digital image that depicts a garment. Candidate appearance flow maps are computed that warp the garment based on the pose at different pixel-block sizes using a first machine learning model. The generator system generates a warped garment image by combining the candidate appearance flow maps as an aggregate per-pixel displacement map using a convolutional gated recurrent network. A conditional segment mask is predicted that segments portions of a geometry of the person using a second machine learning model. The generator system outputs a digital image that depicts the person in the pose wearing the garment based on the warped garment image and the conditional segmentation mask using a third machine learning model.
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