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公开(公告)号：US11250548B2
公开(公告)日：2022-02-15
申请号：US16791939
申请日：2020-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling content in place of the holey digital image's holes.
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72.发明授权公开(公告)号：US10997464B2
公开(公告)日：2021-05-04
申请号：US16186382
申请日：2018-11-09
Applicant: Adobe Inc.
Inventor： Jimei Yang , Jianming Zhang , Aaron Phillip Hertzmann , Jianan Li
Abstract: Digital image layout training is described using wireframe rendering within a generative adversarial network (GAN) system. A GAN system is employed to train the generator module to refine digital image layouts. To do so, a wireframe rendering discriminator module rasterizes a refined digital training digital image layout received from a generator module into a wireframe digital image layout. The wireframe digital image layout is then compared with at least one ground truth digital image layout using a loss function as part of machine learning by the wireframe discriminator module. The generator module is then trained by backpropagating a result of the comparison.
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73.发明申请公开(公告)号：US20200349688A1
公开(公告)日：2020-11-05
申请号：US16930736
申请日：2020-07-16
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.
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74.发明申请公开(公告)号：US20200258204A1
公开(公告)日：2020-08-13
申请号：US16271058
申请日：2019-02-08
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.
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公开(公告)号：US10650581B1
公开(公告)日：2020-05-12
申请号：US16185587
申请日：2018-11-09
Applicant: Adobe Inc.
Inventor： Zhili Chen , Qingyang Li , Jimei Yang
Abstract: A 3D fluid volume generation system obtains a 2D sketch of an outline of a fluid for which the 3D fluid volume is to be generated, and generates a 3D fluid volume that matches the user's sketch. The 3D fluid volume generation system implements a coarse volume generation stage followed by a refinement stage. In the coarse volume generation stage, the 3D fluid volume generation system generates a coarse 3D fluid volume based on the 2D sketch. The coarse 3D fluid volume is referred to as “coarse” because the contour of the coarse 3D fluid volume roughly matches the 2D sketch. In the refinement stage, the coarse 3D fluid volume is refined to better match the 2D sketch, and the 3D fluid volume for the 2D sketch is output.
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Patent Agency Ranking
公开(公告)号：US10593043B2
公开(公告)日：2020-03-17
申请号：US15950087
申请日：2018-04-10
Applicant: Adobe Inc.
Inventor： Zhe Lin , Yibing Song , Xin Lu , Xiaohui Shen , Jimei Yang
IPC: G06T7/13 , G06K9/66 , G06K9/38 , G06K9/00 , G06K9/46 , G06T7/12 , G06N3/04 , G06N7/00 , G06N3/08 , G06T7/11
Abstract: Systems and methods are disclosed for segmenting a digital image to identify an object portrayed in the digital image from background pixels in the digital image. In particular, in one or more embodiments, the disclosed systems and methods use a first neural network and a second neural network to generate image information used to generate a segmentation mask that corresponds to the object portrayed in the digital image. Specifically, in one or more embodiments, the disclosed systems and methods optimize a fit between a mask boundary of the segmentation mask to edges of the object portrayed in the digital image to accurately segment the object within the digital image.
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公开(公告)号：US10475207B2
公开(公告)日：2019-11-12
申请号：US16057161
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Jimei Yang , Yu-Wei Chao , Scott Cohen , Brian Price
Abstract: A forecasting neural network receives data and extracts features from the data. A recurrent neural network included in the forecasting neural network provides forecasted features based on the extracted features. In an embodiment, the forecasting neural network receives an image, and features of the image are extracted. The recurrent neural network forecasts features based on the extracted features, and pose is forecasted based on the forecasted features. Additionally or alternatively, additional poses are forecasted based on additional forecasted features.
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公开(公告)号：US10424064B2
公开(公告)日：2019-09-24
申请号：US15296845
申请日：2016-10-18
Applicant: Adobe Inc.
Inventor： Brian Price , Scott Cohen , Jimei Yang
Abstract: Certain aspects involve semantic segmentation of objects in a digital visual medium by determining a score for each pixel of the digital visual medium that is representative of a likelihood that each pixel corresponds to the objects associated with bounding boxes within the digital visual medium. An instance-level label that yields a label for each of the pixels of the digital visual medium corresponding to the objects is determined based, in part, on a collective probability map including the score for each pixel of the digital visual medium. In some aspects, the score for each pixel corresponding to each bounding box is determined by a prediction model trained by a neural network.
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公开(公告)号：US20190147627A1
公开(公告)日：2019-05-16
申请号：US15814751
申请日：2017-11-16
Applicant: Adobe Inc.
Inventor： Zhili Chen , Zhaowen Wang , Rundong Wu , Jimei Yang
CPC classification number: G06T11/001 , G06N3/0454 , G06N3/08 , G06T9/002 , G06T11/203 , G06T11/40
Abstract: Oil painting simulation techniques are disclosed which simulate painting brush strokes using a trained neural network. In some examples, a method may include inferring a new height map of existing paint on a canvas after a new painting brush stroke is applied based on a bristle trajectory map that represents the new painting brush stroke and a height map of existing paint on the canvas prior to the application of the new painting brush stroke, and generating a rendering of the new painting brush stroke based on the new height map of existing paint on the canvas after the new painting brush stroke is applied to the canvas and a color map.
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公开(公告)号：US10192321B2
公开(公告)日：2019-01-29
申请号：US15409321
申请日：2017-01-18
Applicant: ADOBE INC.
Inventor： Chen Fang , Zhaowen Wang , Yijun Li , Jimei Yang
IPC: G06T15/04 , G06T7/40 , G06T11/00 , G06T5/50 , G06F3/0482
Abstract: Systems and techniques that synthesize an image with similar texture to a selected style image. A generator network is trained to synthesize texture images depending on a selection unit input. The training configures the generator network to synthesize texture images that are similar to individual style images of multiple style images based on which is selected by the selection unit input. The generator network can be configured to minimize a covariance matrix-based style loss and/or a diversity loss in synthesizing the texture images. After training the generator network, the generator network is used to synthesize texture images for selected style images. For example, this can involve receiving user input selecting a selected style image, determining the selection unit input based on the selected style image, and synthesizing texture images using the generator network with the selection unit input and noise input.
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