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公开(公告)号：US12067659B2
公开(公告)日：2024-08-20
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
CPC classification number: G06T13/40 , G06N3/045 , G06N3/08 , G06N3/088 , G06T7/20 , G06T7/73 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30196
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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12.发明公开公开(公告)号：US20240144520A1
公开(公告)日：2024-05-02
申请号：US18304144
申请日：2023-04-20
Applicant: Adobe Inc.
Inventor： Giorgio Gori , Yi Zhou , Yangtuanfeng Wang , Yang Zhou , Krishna Kumar Singh , Jae Shin Yoon , Duygu Ceylan Aksit
IPC: G06T7/73
CPC classification number: G06T7/73 , G06T2207/20084 , G06T2207/30196
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.
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公开(公告)号：US20240135510A1
公开(公告)日：2024-04-25
申请号：US18190513
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 , G06T7/11 , G06T7/40 , G06V10/25 , G06V10/764 , G06V10/82 , G06T2207/20104
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：US20230289970A1
公开(公告)日：2023-09-14
申请号：US17693618
申请日：2022-03-14
Applicant: Adobe Inc.
Inventor： Gaurav Parmar , Krishna Kumar Singh , Yijun Li , Richard Zhang , Jingwan Lu
CPC classification number: G06T7/11 , G06T11/001 , G06T3/4046 , G06T2207/20084 , G06T2207/20081
Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.
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公开(公告)号：US20230094954A1
公开(公告)日：2023-03-30
申请号：US17485780
申请日：2021-09-27
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Sridhar Mahadevan , Moumita Sinha , Md Mehrab Tanjim , Krishna Kumar Singh , David Arbour
Abstract: Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.
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Patent Agency Ranking
公开(公告)号：US20250069203A1
公开(公告)日：2025-02-27
申请号：US18454850
申请日：2023-08-24
Applicant: ADOBE INC.
Inventor： Yuqian Zhou , Krishna Kumar Singh , Benjamin Delarre , Zhe Lin , Jingwan Lu , Taesung Park , Sohrab Amirghodsi , Elya Shechtman
Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation are described. An embodiment of the present disclosure includes obtaining an input image, an inpainting mask, and a plurality of content preservation values corresponding to different regions of the inpainting mask, and identifying a plurality of mask bands of the inpainting mask based on the plurality of content preservation values. An image generation model generates an output image based on the input image and the inpainting mask. The output image is generated in a plurality of phases. Each of the plurality of phases uses a corresponding mask band of the plurality of mask bands as an input.
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公开(公告)号：US20240404013A1
公开(公告)日：2024-12-05
申请号：US18515378
申请日：2023-11-21
Applicant: ADOBE INC.
Inventor： Yuqian Zhou , Krishna Kumar Singh , Zhe Lin , Qing Liu , Zhifei Zhang , Sohrab Amirghodsi , Elya Shechtman , Jingwan Lu
Abstract: Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.
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公开(公告)号：US12159413B2
公开(公告)日：2024-12-03
申请号：US17693618
申请日：2022-03-14
Applicant: Adobe Inc.
Inventor： Gaurav Parmar , Krishna Kumar Singh , Yijun Li , Richard Zhang , Jingwan Lu
IPC: G06T7/11 , G06T3/4046 , G06T11/00
Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.
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公开(公告)号：US20240338799A1
公开(公告)日：2024-10-10
申请号：US18178212
申请日：2023-03-03
Applicant: Adobe Inc.
Inventor： Yijun Li , Richard Zhang , Krishna Kumar Singh , Jingwan Lu , Gaurav Parmar , Jun-Yan Zhu
IPC: G06T5/00 , G06F40/126 , G06T5/50
CPC classification number: G06T5/70 , G06F40/126 , G06T5/50 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.
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20.发明公开公开(公告)号：US20240331236A1
公开(公告)日：2024-10-03
申请号：US18178194
申请日：2023-03-03
Applicant: Adobe Inc.
Inventor： Yijun Li , Richard Zhang , Krishna Kumar Singh , Jingwan Lu , Gaurav Parmar , Jun-Yan Zhu
CPC classification number: G06T11/60 , G06T5/70 , G06T9/00 , G06V10/761 , G06V10/82 , G06V20/70 , G06T2207/20182
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.
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