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公开(公告)号：US11899927B2
公开(公告)日：2024-02-13
申请号：US17648718
申请日：2022-01-24
Applicant: Adobe Inc.
Inventor： Christopher Alan Tensmeyer , Rajiv Jain , Curtis Michael Wigington , Brian Lynn Price , Brian Lafayette Davis
IPC: G06K9/00 , G06F3/04883 , G06N3/08 , G06V30/32 , G06V30/228 , G06V30/226 , G06N3/045 , G06V10/82 , G06V10/44
CPC classification number: G06F3/04883 , G06N3/045 , G06N3/08 , G06V10/454 , G06V10/82 , G06V30/228 , G06V30/2264 , G06V30/2276 , G06V30/347
Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.
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公开(公告)号：US11538170B2
公开(公告)日：2022-12-27
申请号：US16839209
申请日：2020-04-03
Applicant: ADOBE INC.
Inventor： Brian Lynn Price , Scott David Cohen , Henghui Ding
Abstract: Methods and systems are provided for optimal segmentation of an image based on multiple segmentations. In particular, multiple segmentation methods can be combined by taking into account previous segmentations. For instance, an optimal segmentation can be generated by iteratively integrating a previous segmentation (e.g., using an image segmentation method) with a current segmentation (e.g., using the same or different image segmentation method). To allow for optimal segmentation of an image based on multiple segmentations, one or more neural networks can be used. For instance, a convolutional RNN can be used to maintain information related to one or more previous segmentations when transitioning from one segmentation method to the next. The convolutional RNN can combine the previous segmentation(s) with the current segmentation without requiring any information about the image segmentation method(s) used to generate the segmentations.
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公开(公告)号：US11263259B2
公开(公告)日：2022-03-01
申请号：US16929429
申请日：2020-07-15
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Kalyan Krishna Sunkavalli , Hengshuang Zhao , Brian Lynn Price
Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.
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公开(公告)号：US20210256708A1
公开(公告)日：2021-08-19
申请号：US17313158
申请日：2021-05-06
Applicant: Adobe Inc.
Inventor： Brian Lynn Price , Scott Cohen , Marco Forte , Ning Xu
Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.
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公开(公告)号：US10747811B2
公开(公告)日：2020-08-18
申请号：US15986401
申请日：2018-05-22
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Kalyan Krishna Sunkavalli , Hengshuang Zhao , Brian Lynn Price
Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.
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Patent Agency Ranking
公开(公告)号：US20250022099A1
公开(公告)日：2025-01-16
申请号：US18351838
申请日：2023-07-13
Applicant: ADOBE INC.
Inventor： Yizhi Song , Zhifei Zhang , Zhe Lin , Scott Cohen , Brian Lynn Price , Jianming Zhang , Soo Ye Kim
Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.
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公开(公告)号：US12147896B2
公开(公告)日：2024-11-19
申请号：US18296525
申请日：2023-04-06
Applicant: Adobe Inc.
Inventor： Long Mai , Yannick Hold-Geoffroy , Naoto Inoue , Daichi Ito , Brian Lynn Price
Abstract: Embodiments of the present invention provide systems, methods, and non-transitory computer storage media for generating an ambient occlusion (AO) map for a 2D image that can be combined with the 2D image to adjust the contrast of the 2D image based on the geometric information in the 2D image. In embodiments, using a trained neural network, an AO map for a 2D image is automatically generated without any predefined 3D scene information. Optimizing the neural network to generate an estimated AO map for a 2D image requires training, testing, and validating the neural network using a synthetic dataset comprised of pairs of images and ground truth AO maps rendered from 3D scenes. By using an estimated AO map to adjust the contrast of a 2D image, the contrast of the image can be adjusted to make the image appear lifelike by modifying the shadows and shading in the image based on the ambient lighting present in the image.
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公开(公告)号：US20240249413A1
公开(公告)日：2024-07-25
申请号：US18100419
申请日：2023-01-23
Applicant: Adobe Inc.
Inventor： Jason Wen Yong Kuen , Zhe Lin , Sukjun Hwang , Jianming Zhang , Brian Lynn Price
IPC: G06T7/11
CPC classification number: G06T7/11 , G06T2207/20081 , G06T2207/20104
Abstract: In implementations of systems for performing multiple segmentation tasks, a computing device implements a segment system to receive input data describing a digital image depicting an object. The segment system computes per-pixel embeddings for the digital image using a pixel decoder of a machine learning model. Output embeddings are generated using a transformer decoder of the machine learning model based on the per-pixel embeddings for the digital image, input embeddings for a first segmentation task and input embeddings for a second segmentation task. The segment system outputs a first digital image and a second digital image. The first digital image depicts the object segmented based on the first segmentation task and the second digital image depicts the object segmented based on the second segmentation task.
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公开(公告)号：US20230360177A1
公开(公告)日：2023-11-09
申请号：US17736397
申请日：2022-05-04
Applicant: Adobe Inc.
Inventor： Joon-Young Lee , Seoungwug Oh , Brian Lynn Price , Hongje Seong
CPC classification number: G06T5/001 , G06N20/20 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081
Abstract: In implementations of systems for joint trimap estimation and alpha matte prediction, a computing device implements a matting system to estimate a trimap for a frame of a digital video using a first stage of a machine learning model. An alpha matte is predicted for the frame based on the trimap and the frame using a second stage of the machine learning model. The matting system generates a refined trimap and a refined alpha matte for the frame based on the alpha matte, the trimap, and the frame using a third stage of the machine learning model. An additional trimap is estimated for an additional frame of the digital video based on the refined trimap and the refined alpha matte using the first stage of the machine learning model.
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10.发明公开公开(公告)号：US20230244940A1
公开(公告)日：2023-08-03
申请号：US18296525
申请日：2023-04-06
Applicant: Adobe Inc.
Inventor： Long MAI , Yannick Hold-Geoffroy , Naoto Inoue , Daichi Ito , Brian Lynn Price
CPC classification number: G06N3/08 , G06T15/80 , G06T15/506 , G06T5/50 , G06T2207/20084 , G06T2207/10028 , G06T2207/20081
Abstract: Embodiments of the present invention provide systems, methods, and non-transitory computer storage media for generating an ambient occlusion (AO) map for a 2D image that can be combined with the 2D image to adjust the contrast of the 2D image based on the geometric information in the 2D image. In embodiments, using a trained neural network, an AO map for a 2D image is automatically generated without any predefined 3D scene information. Optimizing the neural network to generate an estimated AO map for a 2D image requires training, testing, and validating the neural network using a synthetic dataset comprised of pairs of images and ground truth AO maps rendered from 3D scenes. By using an estimated AO map to adjust the contrast of a 2D image, the contrast of the image can be adjusted to make the image appear lifelike by modifying the shadows and shading in the image based on the ambient lighting present in the image.
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