-
131.发明公开公开(公告)号：US20230259587A1
公开(公告)日：2023-08-17
申请号：US17650967
申请日：2022-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Jingwan Lu , Scott Cohen , Jianming Zhang , Ning Xu , Elya Shechtman , Connelly Barnes , Sohrab Amirghodsi
CPC classification number: G06K9/6257 , G06T5/005 , G06T7/11 , G06N3/08 , G06T2207/20084 , G06T2207/20081
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization
-
公开(公告)号：US20230145498A1
公开(公告)日：2023-05-11
申请号：US17520361
申请日：2021-11-05
Applicant: Adobe Inc.
Inventor： Yunhan Zhao , Connelly Barnes , Yuqian Zhou , Sohrab Amirghodsi , Elya Shechtman
CPC classification number: G06T5/005 , G06T3/0093 , G06T3/4046 , G06T5/50 , G06T7/30 , G06T7/50 , G06T7/90 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately restoring missing pixels within a hole region of a target image utilizing multi-image inpainting techniques based on incorporating geometric depth information. For example, in various implementations, the disclosed systems utilize a depth prediction of a source image as well as camera relative pose parameters. Additionally, in some implementations, the disclosed systems jointly optimize the depth rescaling and camera pose parameters before generating the reprojected image to further increase the accuracy of the reprojected image. Further, in various implementations, the disclosed systems utilize the reprojected image in connection with a content-aware fill model to generate a refined composite image that includes the target image having a hole, where the hole is filled in based on the reprojected image of the source image.
-
公开(公告)号：US11610433B2
公开(公告)日：2023-03-21
申请号：US17154830
申请日：2021-01-21
Applicant: Adobe Inc.
Inventor： Kartik Sethi , Oliver Wang , Tharun Mohandoss , Elya Shechtman , Chetan Nanda
Abstract: In implementations of skin tone assisted digital image color matching, a device implements a color editing system, which includes a facial detection module to detect faces in an input image and in a reference image, and includes a skin tone model to determine a skin tone value reflective of a skin tone of each of the faces. A color matching module can be implemented to group the faces into one or more face groups based on the skin tone value of each of the faces, match a face group pair as an input image face group paired with a reference image face group, and generate a modified image from the input image based on color features of the reference image, the color features including face skin tones of the respective faces in the face group pair as part of the color features applied to modify the input image.
-
公开(公告)号：US11449974B2
公开(公告)日：2022-09-20
申请号：US16678132
申请日：2019-11-08
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Aliakbar Darabi , Elya Shechtman
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating modified digital images by utilizing a patch match algorithm to generate nearest neighbor fields for a second digital image based on a nearest neighbor field associated with a first digital image. For example, the disclosed systems can identify a nearest neighbor field associated with a first digital image of a first resolution. Based on the nearest neighbor field of the first digital image, the disclosed systems can utilize a patch match algorithm to generate a nearest neighbor field for a second digital image of a second resolution larger than the first resolution. The disclosed systems can further generate a modified digital image by filling a target region of the second digital image utilizing the generated nearest neighbor field.
-
公开(公告)号：US20220292649A1
公开(公告)日：2022-09-15
申请号：US17196581
申请日：2021-03-09
Applicant: Adobe Inc.
Inventor： Oliver Wang , John Nelson , Geoffrey Oxholm , Elya Shechtman
Abstract: Certain aspects involve video inpainting in which content is propagated from a user-provided reference video frame to other video frames depicting a scene. One example method includes one or more processing devices that performs operations that include accessing a scene depicting a reference object that includes an annotation identifying a target region to be modified in one or more video frames. The operations also includes computing a target motion of a target pixel that is subject to a motion constraint. The motion constraint is based on a three-dimensional model of the reference object. Further, operations include determining color data of the target pixel to correspond to the target motion. The color data includes a color value and a gradient. Operations also include determining gradient constraints using gradient values of neighbor pixels. Additionally, the processing devices updates the color data of the target pixel subject to the gradient constraints.
-
Patent Agency Ranking
公开(公告)号：US20220254071A1
公开(公告)日：2022-08-11
申请号：US17163284
申请日：2021-01-29
Applicant: Adobe Inc.
Inventor： Utkarsh Ojha , Yijun Li , Richard Zhang , Jingwan Lu , Elya Shechtman , Alexei A. Efros
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently modifying a generative adversarial neural network using few-shot adaptation to generate digital images corresponding to a target domain while maintaining diversity of a source domain and realism of the target domain. In particular, the disclosed systems utilize a generative adversarial neural network with parameters learned from a large source domain. The disclosed systems preserve relative similarities and differences between digital images in the source domain using a cross-domain distance consistency loss. In addition, the disclosed systems utilize an anchor-based strategy to encourage different levels or measures of realism over digital images generated from latent vectors in different regions of a latent space.
-
公开(公告)号：US20220122308A1
公开(公告)日：2022-04-21
申请号：US17468546
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 seamlessly blend edited and unedited regions of an image. A computing system crops an input image around a region to be edited. The system applies an affine transformation to rotate the cropped input image. The system provides the rotated cropped input image as input to a machine learning model to generate a latent space representation of the rotated cropped input image. The system edits the latent space representation and provides the edited latent space representation to a generator neural network to generate a generated edited image. The system applies an inverse affine transformation to rotate the generated edited image and aligns an identified segment of the rotated generated edited image with an identified corresponding segment of the input image to produce an aligned rotated generated edited image. The system blends the aligned rotated generated edited image with the input image to generate an edited output image.
-
公开(公告)号：US20210358177A1
公开(公告)日：2021-11-18
申请号：US16874399
申请日：2020-05-14
Applicant: Adobe Inc.
Inventor： Taesung Park , Richard Zhang , Oliver Wang , Junyan Zhu , Jingwan Lu , Elya Shechtman , Alexei A Efros
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified digital image from extracted spatial and global codes. For example, the disclosed systems can utilize a global and spatial autoencoder to extract spatial codes and global codes from digital images. The disclosed systems can further utilize the global and spatial autoencoder to generate a modified digital image by combining extracted spatial and global codes in various ways for various applications such as style swapping, style blending, and attribute editing.
-
公开(公告)号：US11178368B2
公开(公告)日：2021-11-16
申请号：US16696160
申请日：2019-11-26
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.
-
公开(公告)号：US20210158570A1
公开(公告)日：2021-05-27
申请号：US16692503
申请日：2019-11-22
Applicant: Adobe Inc.
Inventor： Tharun Mohandoss , Pulkit Gera , Oliver Wang , Kartik Sethi , Kalyan Sunkavalli , Elya Shechtman , Chetan Nanda
Abstract: This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.
-
-
-
-
-
-
-
-
-
Search Results
198
records
(took 0.028 seconds)
