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公开(公告)号：US11069030B2
公开(公告)日：2021-07-20
申请号：US15928706
申请日：2018-03-22
Applicant: ADOBE INC.
Inventor： Xiaohui Shen , Zhe Lin , Xin Lu , Sarah Aye Kong , I-Ming Pao , Yingcong Chen
Abstract: Methods and systems are provided for generating enhanced image. A neural network system is trained where the training includes training a first neural network that generates enhanced images conditioned on content of an image undergoing enhancement and training a second neural network that designates realism of the enhanced images generated by the first neural network. The neural network system is trained by determine loss and accordingly adjusting the appropriate neural network(s). The trained neural network system is used to generate an enhanced aesthetic image from a selected image where the output enhanced aesthetic image has increased aesthetics when compared to the selected image.
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公开(公告)号：US20200175736A1
公开(公告)日：2020-06-04
申请号：US16205010
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Yumin Jia , Xin Lu , Jen-Chan Chien
Abstract: Facial skin mask generated by a digital content creation system is described. The digital content creation system includes digital effects on skin in facial regions of digital content with efficiency and accuracy. Upon identifying a facial region within digital content, the system generates a first regional skin mask, a second regional skin mask, and combines both of the first and second regional skin masks to generate a facial skin mask indicative of skin of the identified facial regions depicted in digital content. The digital content creation system then modifies digital content by applying user selected digital effects to the skin of the facial region using the generated facial skin mask.
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公开(公告)号：US20190333198A1
公开(公告)日：2019-10-31
申请号：US15962735
申请日：2018-04-25
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
IPC: G06T5/50
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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公开(公告)号：US10460214B2
公开(公告)日：2019-10-29
申请号：US15799395
申请日：2017-10-31
Applicant: Adobe Inc.
Inventor： Xin Lu , Zhe Lin , Xiaohui Shen , Jimei Yang , Jianming Zhang , Jen-Chan Jeff Chien , Chenxi Liu
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or more salient content neural networks. In particular, in one or more embodiments, the disclosed systems and methods train one or more salient content neural networks to efficiently identify foreground pixels in digital visual media. Moreover, in one or more embodiments, the disclosed systems and methods provide a trained salient content neural network to a mobile device, allowing the mobile device to directly select salient objects in digital visual media utilizing a trained neural network. Furthermore, in one or more embodiments, the disclosed systems and methods train and provide multiple salient content neural networks, such that mobile devices can identify objects in real-time digital visual media feeds (utilizing a first salient content neural network) and identify objects in static digital images (utilizing a second salient content neural network).
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公开(公告)号：US20190287283A1
公开(公告)日：2019-09-19
申请号：US15921998
申请日：2018-03-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Certain embodiments involve using an image completion neural network to perform user-guided image completion. For example, an image editing application accesses an input image having a completion region to be replaced with new image content. The image editing application also receives a guidance input that is applied to a portion of a completion region. The image editing application provides the input image and the guidance input to an image completion neural network that is trained to perform image-completion operations using guidance input. The image editing application produces a modified image by replacing the completion region of the input image with the new image content generated with the image completion network. The image editing application outputs the modified image having the new image content.
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Patent Agency Ranking
公开(公告)号：US20190147305A1
公开(公告)日：2019-05-16
申请号：US15812695
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Xin Lu , Zejun Huang , Jen-Chan Jeff Chien
Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that automatically select an image from a plurality of images based on the multi-context aware rating of the image. In particular, systems described herein can generate a plurality of probability context scores for an image. Moreover, the disclosed systems can generate a plurality of context-specific scores for an image. Utilizing each of the probability context scores and each of the corresponding context-specific scores for an image, the disclosed systems can generate a multi-context aware rating for the image. Thereafter, the disclosed systems can select an image from the plurality of images with the highest multi-context aware rating for delivery to the user. The disclosed system can utilize one or more neural networks to both generate the probability context scores for an image and to generate the context-specific scores for an image.
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公开(公告)号：US20250005884A1
公开(公告)日：2025-01-02
申请号：US18215551
申请日：2023-06-28
Applicant: Adobe Inc.
Inventor： Zichuan Liu , Xin Lu , Mingyuan Wu
Abstract: In implementations of systems for efficient object segmentation, a computing device implements a segment system to receive a user input specifying coordinates of a digital image. The segment system computes receptive fields of a machine learning model based on the coordinates of the digital image. The machine learning model is trained on training data to generate segment masks for objects depicted in digital images. The segment system processes a portion of a feature map of the digital image using the machine learning model based on the receptive fields. A segment mask is generated for an object depicted in the digital image based on processing the portion of the feature map of the digital image using the machine learning model.
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公开(公告)号：US11625813B2
公开(公告)日：2023-04-11
申请号：US17085491
申请日：2020-10-30
Applicant: Adobe Inc.
Inventor： Sheng-Wei Huang , Wentian Zhao , Kun Wan , Zichuan Liu , Xin Lu , Jen-Chan Jeff Chien
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a 3D to 2D generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.
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公开(公告)号：US11055887B2
公开(公告)日：2021-07-06
申请号：US16205010
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Yumin Jia , Xin Lu , Jen-Chan Chien
Abstract: Facial skin mask generated by a digital content creation system is described. The digital content creation system includes digital effects on skin in facial regions of digital content with efficiency and accuracy. Upon identifying a facial region within digital content, the system generates a first regional skin mask, a second regional skin mask, and combines both of the first and second regional skin masks to generate a facial skin mask indicative of skin of the identified facial regions depicted in digital content. The digital content creation system then modifies digital content by applying user selected digital effects to the skin of the facial region using the generated facial skin mask.
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30.发明申请公开(公告)号：US20200372622A1
公开(公告)日：2020-11-26
申请号：US16984992
申请日：2020-08-04
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
IPC: G06T5/50
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short- exposure images without additional information.
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