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公开(公告)号：US20250078349A1
公开(公告)日：2025-03-06
申请号：US18459526
申请日：2023-09-01
Applicant: ADOBE INC.
Inventor： Wonwoong Cho , Hareesh Ravi , Midhun Harikumar , Vinh Ngoc Khuc , Krishna Kumar Singh , Jingwan Lu , Ajinkya Gorakhnath Kale
Abstract: A method, apparatus, and non-transitory computer readable medium for image generation are described. Embodiments of the present disclosure obtain a content input and a style input via a user interface or from a database. The content input includes a target spatial layout and the style input includes a target style. A content encoder of an image processing apparatus encodes the content input to obtain a spatial layout mask representing the target spatial layout. A style encoder of the image processing apparatus encodes the style input to obtain a style embedding representing the target style. An image generation model of the image processing apparatus generates an image based on the spatial layout mask and the style embedding, where the image includes the target spatial layout and the target style.
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公开(公告)号：US20250037431A1
公开(公告)日：2025-01-30
申请号：US18357621
申请日：2023-07-24
Applicant: ADOBE INC.
Inventor： Min Jin Chong , Krishna Kumar Singh , Yijun Li , Jingwan Lu
IPC: G06V10/774 , G06N3/045 , G06N3/0475
Abstract: Systems and methods for training a Generative Adversarial Network (GAN) using feature regularization are described herein. Embodiments are configured to generate a candidate image using a generator network of a GAN, classify the candidate image as real or generated using a discriminator network of the GAN, and train the GAN to generate realistic images based on the classifying of the candidate image. The training process includes regularizing a gradient with respect to features extracted using a discriminator network of the GAN.
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3.发明公开公开(公告)号：US20240135513A1
公开(公告)日：2024-04-25
申请号：US18190654
申请日：2023-03-27
Applicant: Adobe Inc.
Inventor： Krishna Kumar Singh , Yijun Li , Jingwan Lu , Duygu Ceylan Aksit , Yangtuanfeng Wang , Jimei Yang , Tobias Hinz
CPC classification number: G06T5/005 , G06T3/0093 , G06T7/40 , G06T7/70 , G06V10/44 , G06V10/771 , G06V10/806 , G06V10/82 , G06T2207/30196
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.
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4.发明公开公开(公告)号：US20240135509A1
公开(公告)日：2024-04-25
申请号：US18190500
申请日：2023-03-27
Applicant: Adobe Inc.
Inventor： Qing Liu , Jianming Zhang , Krishna Kumar Singh , Scott Cohen , Zhe Lin
CPC classification number: G06T5/005 , G06T5/002 , G06T7/11 , G06T11/60 , G06V10/764 , G06V10/82 , G06V20/70 , G06T2200/24 , G06T2207/20021 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.
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公开(公告)号：US11861762B2
公开(公告)日：2024-01-02
申请号：US17400474
申请日：2021-08-12
Applicant: Adobe Inc.
Inventor： Yuheng Li , Yijun Li , Jingwan Lu , Elya Shechtman , Krishna Kumar Singh
IPC: G06T11/00
CPC classification number: G06T11/00 , G06T2210/12
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.
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Patent Agency Ranking
公开(公告)号：US20230123820A1
公开(公告)日：2023-04-20
申请号：US17502714
申请日：2021-10-15
Applicant: Adobe Inc.
Inventor： Yangtuanfeng Wang , Duygu Ceylan Aksit , Krishna Kumar Singh , Niloy J Mitra
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and method that utilize a character animation neural network informed by motion and pose signatures to generate a digital video through person-specific appearance modeling and motion retargeting. In particular embodiments, the disclosed systems implement a character animation neural network that includes a pose embedding model to encode a pose signature into spatial pose features. The character animation neural network further includes a motion embedding model to encode a motion signature into motion features. In some embodiments, the disclosed systems utilize the motion features to refine per-frame pose features and improve temporal coherency. In certain implementations, the disclosed systems also utilize the motion features to demodulate neural network weights used to generate an image frame of a character in motion based on the refined pose features.
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公开(公告)号：US20250078406A1
公开(公告)日：2025-03-06
申请号：US18242380
申请日：2023-09-05
Applicant: Adobe Inc.
Inventor： Jae Shin Yoon , Yangtuanfeng Wang , Krishna Kumar Singh , Junying Wang , Jingwan Lu
Abstract: A modeling system accesses a two-dimensional (2D) input image displayed via a user interface, the 2D input image depicting, at a first view, a first object. At least one region of the first object is not represented by pixel values of the 2D input image. The modeling system generates, by applying a 3D representation generation model to the 2D input image, a three-dimensional (3D) representation of the first object that depicts an entirety of the first object including the first region. The modeling system displays, via the user interface, the 3D representation, wherein the 3D representation is viewable via the user interface from a plurality of views including the first view.
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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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