Patents by Inventor Mayur Hemani
Mayur Hemani has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250005812Abstract: 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.Type: ApplicationFiled: June 28, 2023Publication date: January 2, 2025Applicant: Adobe Inc.Inventors: Rishabh Jain, Mayur Hemani, Mausoom Sarkar, Krishna Kumar Singh, Jingwan Lu, Duygu Ceylan Aksit, Balaji Krishnamurthy
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Publication number: 20250005824Abstract: 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.Type: ApplicationFiled: June 27, 2023Publication date: January 2, 2025Inventors: Rishabh Jain, Mayur Hemani, Duygu Ceylan Aksit, Krishna Kumar Singh, Jingwan Lu, Mausoom Sarkar, Balaji Krishnamurthy
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Publication number: 20240428564Abstract: 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.Type: ApplicationFiled: June 22, 2023Publication date: December 26, 2024Applicant: Adobe Inc.Inventors: Rishabh Jain, Mayur Hemani, Mausoom Sarkar, Krishna Kumar Singh, Jingwan Lu, Duygu Ceylan Aksit, Balaji Krishnamurthy
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Publication number: 20240395024Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for debiasing vision-language models utilizing additive residual learning. In particular, in one or more embodiments, the disclosed systems generate an encoded image representation of a digital image utilizing an image encoder of a vision-language neural network. Additionally, in some embodiments, the disclosed systems extract a protected attribute encoding from the encoded image representation of the digital image utilizing an additive residual learner. Upon extracting the protected attribute encoding, in some implementations, the disclosed systems determine a debiased image encoding for the digital image by combining the protected attribute encoding and the encoded image representation.Type: ApplicationFiled: May 23, 2023Publication date: November 28, 2024Inventors: Mayur Hemani, Chirag Agarwal, Ashish Seth
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Publication number: 20240378912Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image.Type: ApplicationFiled: May 12, 2023Publication date: November 14, 2024Inventors: Mausoom Sarkar, Nikitha S R, Mayur Hemani, Rishabh Jain, Balaji Krishnamurthy
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Publication number: 20240362427Abstract: 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.Type: ApplicationFiled: April 28, 2023Publication date: October 31, 2024Applicant: Adobe Inc.Inventors: Mukul Gupta, Yaman Kumar, Rahul Gupta, Prerna Bothra, Mayur Hemani, Mayank Gupta, Gaurav Makkar
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Patent number: 11861772Abstract: 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.Type: GrantFiled: February 23, 2022Date of Patent: January 2, 2024Assignee: Adobe Inc.Inventors: Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy
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Publication number: 20230267663Abstract: 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.Type: ApplicationFiled: February 23, 2022Publication date: August 24, 2023Applicant: Adobe Inc.Inventors: Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy
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Patent number: 11645786Abstract: 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.Type: GrantFiled: March 11, 2022Date of Patent: May 9, 2023Assignee: Adobe Inc.Inventors: Meet Patel, Mayur Hemani, Karanjeet Singh, Amit Gupta, Apoorva Gupta, Balaji Krishnamurthy
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Publication number: 20220198717Abstract: 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.Type: ApplicationFiled: March 11, 2022Publication date: June 23, 2022Inventors: Meet Patel, Mayur Hemani, Karanjeet Singh, Amit Gupta, Apoorva Gupta, Balaji Krishnamurthy
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Patent number: 11367271Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.Type: GrantFiled: June 19, 2020Date of Patent: June 21, 2022Assignee: Adobe Inc.Inventors: Mayur Hemani, Siddhartha Gairola, Ayush Chopra, Balaji Krishnamurthy, Jonas Dahl
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Patent number: 11335033Abstract: 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.Type: GrantFiled: September 25, 2020Date of Patent: May 17, 2022Assignee: Adobe Inc.Inventors: Meet Patel, Mayur Hemani, Karanjeet Singh, Amit Gupta, Apoorva Gupta, Balaji Krishnamurthy
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Patent number: 11301506Abstract: Automated digital asset tagging techniques and systems are described that support use of multiple vocabulary sets. In one example, a plurality of digital assets are obtained having first-vocabulary tags taken from a first-vocabulary set. Second-vocabulary tags taken from a second-vocabulary set are assigned to the plurality of digital assets through machine learning. A determination is made that at least one first-vocabulary tag includes a plurality of visual classes based on the assignment of at least one second-vocabulary tag. Digital assets are collected from the plurality of digital assets that correspond to one visual class of the plurality of visual classes. The model is generated using machine learning based on the collected digital assets.Type: GrantFiled: June 29, 2017Date of Patent: April 12, 2022Assignee: Adobe Inc.Inventors: Mayur Hemani, Balaji Krishnamurthy
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Publication number: 20220101564Abstract: 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.Type: ApplicationFiled: September 25, 2020Publication date: March 31, 2022Inventors: Meet Patel, Mayur Hemani, Karanjeet Singh, Amit Gupta, Apoorva Gupta, Balaji Krishnamurthy
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Publication number: 20210397876Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.Type: ApplicationFiled: June 19, 2020Publication date: December 23, 2021Inventors: Mayur Hemani, Siddhartha Gairola, Ayush Chopra, Balaji Krishnamurthy, Jonas Dahl
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Patent number: 11080817Abstract: Generating a synthesized image of a person wearing clothing is described. A two-dimensional reference image depicting a person wearing an article of clothing and a two-dimensional image of target clothing in which the person is to be depicted as wearing are received. To generate the synthesized image, a warped image of the target clothing is generated via a geometric matching module, which implements a machine learning model trained to recognize similarities between warped and non-warped clothing images using multi-scale patch adversarial loss. The multi-scale patch adversarial loss is determined by sampling patches of different sizes from corresponding locations of warped and non-warped clothing images. The synthesized image is generated on a per-person basis, such that the target clothing fits the particular body shape, pose, and unique characteristics of the person.Type: GrantFiled: November 4, 2019Date of Patent: August 3, 2021Assignee: Adobe Inc.Inventors: Kumar Ayush, Surgan Jandial, Mayur Hemani, Balaji Krishnamurthy, Ayush Chopra
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Patent number: 11030782Abstract: 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.Type: GrantFiled: November 9, 2019Date of Patent: June 8, 2021Assignee: ADOBE INC.Inventors: Kumar Ayush, Surgan Jandial, Abhijeet Kumar, Mayur Hemani, Balaji Krishnamurthy, Ayush Chopra
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Publication number: 20210142539Abstract: 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.Type: ApplicationFiled: November 9, 2019Publication date: May 13, 2021Inventors: Kumar Ayush, Surgan Jandial, Abhijeet Kumar, Mayur Hemani, Balaji Krishnamurthy, Ayush Chopra
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Publication number: 20210133919Abstract: Generating a synthesized image of a person wearing clothing is described. A two-dimensional reference image depicting a person wearing an article of clothing and a two-dimensional image of target clothing in which the person is to be depicted as wearing are received. To generate the synthesized image, a warped image of the target clothing is generated via a geometric matching module, which implements a machine learning model trained to recognize similarities between warped and non-warped clothing images using multi-scale patch adversarial loss. The multi-scale patch adversarial loss is determined by sampling patches of different sizes from corresponding locations of warped and non-warped clothing images. The synthesized image is generated on a per-person basis, such that the target clothing fits the particular body shape, pose, and unique characteristics of the person.Type: ApplicationFiled: November 4, 2019Publication date: May 6, 2021Applicant: Adobe Inc.Inventors: Kumar Ayush, Surgan Jandial, Mayur Hemani, Balaji Krishnamurthy, Ayush Chopra
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Patent number: 10656625Abstract: A computer implemented method and apparatus for preserving structural integrity of 3-D models when printing at varying scales, by use of a cueing model.Type: GrantFiled: October 12, 2017Date of Patent: May 19, 2020Assignee: ADOBE INC.Inventors: Abhishek Kumar, Naveen Prakash Goel, Mayur Hemani