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51.发明公开公开(公告)号：US20230260175A1
公开(公告)日：2023-08-17
申请号：US17650957
申请日：2022-02-14
Applicant: Adobe Inc.
Inventor： Nadav Epstein , Alexei A. Efros , Taesung Park , Richard Zhang , Elya Shechtman
CPC classification number: G06T11/60 , G06T7/90 , G06T2207/20084 , G06T2207/20212
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images depicting photorealistic scenes utilizing a digital image collaging neural network. For example, the disclosed systems utilize a digital image collaging neural network having a particular architecture for disentangling generation of scene layouts and pixel colors for different regions of a digital image. In some cases, the disclosed systems break down the process of generating a collage digital into generating images representing different regions such as a background and a foreground to be collaged into a final result. For example, utilizing the digital image collaging neural network, the disclosed systems determine scene layouts and pixel colors for both foreground digital images and background digital images to ultimately collage the foreground and background together into a collage digital image depicting a real-world scene.
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公开(公告)号：US20230214967A1
公开(公告)日：2023-07-06
申请号：US18089218
申请日：2022-12-27
Applicant: Adobe Inc.
Inventor： Yuqian Zhou , Elya Shechtman , Connelly Stuart Barnes , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06T5/50 , G06T3/0093 , G06N3/08 , G06T2207/10024 , G06T2207/20224 , G06T2207/20084 , G06T2207/20081 , G06T2207/20221
Abstract: Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.
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公开(公告)号：US20230162407A1
公开(公告)日：2023-05-25
申请号：US17455796
申请日：2021-11-19
Applicant: ADOBE INC.
Inventor： Ratheesh Kalarot , Timothy M. Converse , Shabnam Ghadar , John Thomas Nack , Jingwan Lu , Elya Shechtman , Baldo Faieta , Akhilesh Kumar
CPC classification number: G06T11/00 , G06K9/00288 , G06K9/00268 , G06N3/08
Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).
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54.发明授权公开(公告)号：US11625875B2
公开(公告)日：2023-04-11
申请号：US17091416
申请日：2020-11-06
Applicant: Adobe Inc.
Inventor： Taesung Park , Alexei A. Efros , Elya Shechtman , Richard Zhang , Junyan Zhu
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating modified digital images utilizing a novel swapping autoencoder that incorporates scene layout. In particular, the disclosed systems can receive a scene layout map that indicates or defines locations for displaying specific digital content within a digital image. In addition, the disclosed systems can utilize the scene layout map to guide combining portions of digital image latent code to generate a modified digital image with a particular textural appearance and a particular geometric structure defined by the scene layout map. Additionally, the disclosed systems can utilize a scene layout map that defines a portion of a digital image to modify by, for instance, adding new digital content to the digital image, and can generate a modified digital image depicting the new digital content.
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55.发明申请公开(公告)号：US20230053588A1
公开(公告)日：2023-02-23
申请号：US17400426
申请日：2021-08-12
Applicant: Adobe Inc.
Inventor： Yuheng Li , Yijun Li , Jingwan Lu , Elya Shechtman , Krishna Kumar Singh
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.
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Patent Agency Ranking
公开(公告)号：US20220182588A1
公开(公告)日：2022-06-09
申请号：US17526998
申请日：2021-11-15
Applicant: Adobe Inc.
Inventor： Pulkit Gera , Oliver Wang , Kalyan Krishna Sunkavalli , Elya Shechtman , Chetan Nanda
Abstract: Systems and techniques for automatic digital parameter adjustment are described that leverage insights learned from an image set to automatically predict parameter values for an input item of digital visual content. To do so, the automatic digital parameter adjustment techniques described herein captures visual and contextual features of digital visual content to determine balanced visual output in a range of visual scenes and settings. The visual and contextual features of digital visual content are used to train a parameter adjustment model through machine learning techniques that captures feature patterns and interactions. The parameter adjustment model exploits these feature interactions to determine visually pleasing parameter values for an input item of digital visual content. The predicted parameter values are output, allowing further adjustment to the parameter values.
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57.发明申请公开(公告)号：US20220148241A1
公开(公告)日：2022-05-12
申请号：US17091416
申请日：2020-11-06
Applicant: Adobe Inc.
Inventor： Taesung Park , Alexei A. Efros , Elya Shechtman , Richard Zhang , Junyan Zhu
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating modified digital images utilizing a novel swapping autoencoder that incorporates scene layout. In particular, the disclosed systems can receive a scene layout map that indicates or defines locations for displaying specific digital content within a digital image. In addition, the disclosed systems can utilize the scene layout map to guide combining portions of digital image latent code to generate a modified digital image with a particular textural appearance and a particular geometric structure defined by the scene layout map. Additionally, the disclosed systems can utilize a scene layout map that defines a portion of a digital image to modify by, for instance, adding new digital content to the digital image, and can generate a modified digital image depicting the new digital content.
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公开(公告)号：US20220122307A1
公开(公告)日：2022-04-21
申请号：US17468511
申请日：2021-09-07
Applicant: Adobe Inc.
Inventor： Ratheesh Kalarot , Kevin Wampler , Jingwan Lu , Jakub Fiser , Elya Shechtman , Aliakbar Darabi , Alexandru Vasile Costin
Abstract: Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.
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公开(公告)号：US11270415B2
公开(公告)日：2022-03-08
申请号：US16548498
申请日：2019-08-22
Applicant: Adobe Inc.
Inventor： Connelly Barnes , Sohrab Amirghodsi , Elya Shechtman
Abstract: Techniques are disclosed for filling or otherwise replacing a target region of a primary image with a corresponding region of an auxiliary image. The filling or replacing can be done with an overlay (no subtractive process need be run on the primary image). Because the primary and auxiliary images may not be aligned, both geometric and photometric transformations are applied to the primary and/or auxiliary images. For instance, a geometric transformation of the auxiliary image is performed, to better align features of the auxiliary image with corresponding features of the primary image. Also, a photometric transformation of the auxiliary image is performed, to better match color of one or more pixels of the auxiliary image with color of corresponding one or more pixels of the primary image. The corresponding region of the transformed auxiliary image is then copied and overlaid on the target region of the primary image.
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公开(公告)号：US11200645B2
公开(公告)日：2021-12-14
申请号：US16878182
申请日：2020-05-19
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Sarah Jane Stuckey , Elya Shechtman
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware fill using similarity transformed patches. A user interface receives a user-specified hole and a user-specified sampling region, both of which may be stored in a constraint mask. A brush tool can be used to interactively brush the sampling region and modify the constraint mask. The mask is passed to a patch-based synthesizer configured to synthesize the fill using similarity transformed patches sampled from the sampling region. Fill properties such as similarity transform parameters can be set to control the manner in which the fill is synthesized. A live preview can be provided with gradual updates of the synthesized fill prior to completion. Once a fill has been synthesized, the user interface presents the original image, replacing the hole with the synthesized fill.
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