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公开(公告)号：US20210248432A1
公开(公告)日：2021-08-12
申请号：US16788781
申请日：2020-02-12
Applicant: ADOBE INC.
Inventor： Zhaowen Wang , Zhifei Zhang , Xuan Li , Matthew Fisher , Hailin Jin
IPC: G06K15/02
Abstract: Systems and methods provide for generating glyph initiations using a generative font system. A glyph variant may be generated based on an input vector glyph. A plurality of line segments may be approximated using a differentiable rasterizer with the plurality of line segments representing the contours of the glyph variant. A bitmap of the glyph variant may then be generated based on the line segments. The image loss between the bitmap and a rasterized representation of a vector glyph may be calculated and provided to the generative font system. Based on the image loss, a refined glyph variant may be provided to a user.
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公开(公告)号：US11003831B2
公开(公告)日：2021-05-11
申请号：US15729855
申请日：2017-10-11
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Hailin Jin , Aaron Phillip Hertzmann , Shuhui Jiang
IPC: G06F40/109 , G06N3/04 , G06K9/62 , G06F9/451 , G06N3/08
Abstract: The present disclosure relates to an asymmetric font pairing system that efficiently pairs digital fonts. For example, in one or more embodiments, the asymmetric font pairing system automatically identifies and provides users with visually aesthetic font pairs for use in different sections of an electronic document. In particular, the asymmetric font pairing system learns visually aesthetic font pairs using joint symmetric and asymmetric compatibility metric learning. In addition, the asymmetric font pairing system provides compact compatibility spaces (e.g., a symmetric compatibility space and an asymmetric compatibility space) to computing devices (e.g., client devices and server devices), which enable the computing devices to quickly and efficiently provide font pairs to users.
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公开(公告)号：US20210118207A1
公开(公告)日：2021-04-22
申请号：US16656132
申请日：2019-10-17
Applicant: Adobe Inc.
Inventor： Nirmal Kumawat , Zhaowen Wang
IPC: G06T11/20
Abstract: Automatic font synthesis for modifying a local font to have an appearance that is visually similar to a source font is described. A font modification system receives an electronic document including the source font together with an indication of a font descriptor for the source font. The font descriptor includes information describing various font attributes for the source font, which define a visual appearance of the source font. Using the source font descriptor, the font modification system identifies a local font that is visually similar in appearance to the source font by comparing local font descriptors to the source font descriptor. A visually similar font is then synthesized by modifying glyph outlines of the local font to achieve the visual appearance defined by the source font descriptor. The synthesized font is then used to replace the source font and output in the electronic document at the computing device.
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公开(公告)号：US10970765B2
公开(公告)日：2021-04-06
申请号：US15897856
申请日：2018-02-15
Applicant: Adobe Inc. , The Regents of the University of California
Inventor： Chen Fang , Zhaowen Wang , Wangcheng Kang , Julian McAuley
IPC: G06Q30/00 , G06Q30/06 , G06N3/08 , G06F16/532
Abstract: The present disclosure relates to a personalized fashion generation system that synthesizes user-customized images using deep learning techniques based on visually-aware user preferences. In particular, the personalized fashion generation system employs an image generative adversarial neural network and a personalized preference network to synthesize new fashion items that are individually customized for a user. Additionally, the personalized fashion generation system can modify existing fashion items to tailor the fashion items to a user's tastes and preferences.
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15.发明申请公开(公告)号：US20200311186A1
公开(公告)日：2020-10-01
申请号：US16369893
申请日：2019-03-29
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Tianlang Chen , Ning Xu , Hailin Jin
Abstract: The present disclosure relates to a font retrieval system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the font retrieval system jointly utilizes a combined recognition/retrieval model to generate font affinity scores corresponding to a list of font tags. Further, based on the font affinity scores, the font retrieval system identifies one or more fonts to recommend in response to the list of font tags such that the one or more provided fonts fairly reflect each of the font tags. Indeed, the font retrieval system utilizes a trained font retrieval neural network to efficiently and accurately identify and retrieve fonts in response to a text font tag query.
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Patent Agency Ranking
公开(公告)号：US10783622B2
公开(公告)日：2020-09-22
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
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：US10783408B2
公开(公告)日：2020-09-22
申请号：US15626749
申请日：2017-06-19
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Sarah Aye Kong , I-Ming Pao , Hailin Jin , Alan Lee Erickson
IPC: G06K9/68 , G06T7/11 , G06K9/18 , G06K9/66 , G06N3/02 , G06K9/62 , G06N3/08 , G06N3/04 , G06K9/46
Abstract: Systems and techniques for identification of fonts include receiving a selection of an area of an image including text, where the selection is received from within an application. The selected area of the image is input to a font matching module within the application. The font matching module identifies one or more fonts similar to the text in the selected area using a convolutional neural network. The one or more fonts similar to the text are displayed within the application and the selection and use of the one or more fonts is enabled within the application.
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公开(公告)号：US10467508B2
公开(公告)日：2019-11-05
申请号：US15962514
申请日：2018-04-25
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
IPC: G06K9/68 , G06K9/00 , G06K9/66 , G06K9/46 , G06T3/40 , G06K9/52 , G06T7/60 , G06K9/62 , G06N3/04
Abstract: Font recognition and similarity determination techniques and systems are described. In a first example, localization techniques are described to train a model using machine learning (e.g., a convolutional neural network) using training images. The model is then used to localize text in a subsequently received image, and may do so automatically and without user intervention, e.g., without specifying any of the edges of a bounding box. In a second example, a deep neural network is directly learned as an embedding function of a model that is usable to determine font similarity. In a third example, techniques are described that leverage attributes described in metadata associated with fonts as part of font recognition and similarity determinations.
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公开(公告)号：US20190114742A1
公开(公告)日：2019-04-18
申请号：US15784039
申请日：2017-10-13
Applicant: Adobe Inc.
Inventor： Zhaowen Wang
Abstract: Systems and techniques for converting a low resolution image to a high resolution image include receiving a low resolution image having one or more noise artifacts at a neural network. A noise reduction level is received at the neural network. The neural network determines a network parameter based on the noise reduction level. The neural network converts the low resolution image to a high resolution image and removes one or more of the noise artifacts from the low resolution image during the converting by the using the network parameter. The neural network outputs the high resolution image.
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公开(公告)号：US20240303870A1
公开(公告)日：2024-09-12
申请号：US18179487
申请日：2023-03-07
Applicant: ADOBE INC.
Inventor： Defu Cao , Zhaowen Wang , Jose Ignacio Echevarria Vallespi
CPC classification number: G06T9/002 , G06T7/155 , G06T7/60 , G06T11/00 , G06T2207/20044 , G06T2207/20072 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods for generating representations for vector graphics are described. Embodiments are configured to obtain semantic information and geometric information for a vector graphics image. The semantic information describes individual segments of the vector graphics image, and the geometric information describes geometric relationships among the individual segments. Embodiments are additionally configured to encode the semantic information and the geometric information to obtain a vector graphics representation for the vector graphics image, and to provide a reconstructed image based on the vector graphics representation.
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