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公开(公告)号：US20200342646A1
公开(公告)日：2020-10-29
申请号：US16392041
申请日：2019-04-23
Applicant: ADOBE INC.
Inventor： Zhaowen Wang , Yipin Zhou , Trung Bui , Chen Fang
Abstract: The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.
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公开(公告)号：US10803231B1
公开(公告)日：2020-10-13
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 describes 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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公开(公告)号：US10783409B2
公开(公告)日：2020-09-22
申请号：US16502608
申请日：2019-07-03
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
Abstract: Font replacement based on visual similarity is described. In one or more embodiments, a font descriptor includes multiple font features derived from a visual appearance of a font by a font visual similarity model. The font visual similarity model can be trained using a machine learning system that recognizes similarity between visual appearances of two different fonts. A source computing device embeds a font descriptor in a document, which is transmitted to a destination computing device. The destination compares the embedded font descriptor to font descriptors corresponding to local fonts. Based on distances between the embedded and the local font descriptors, at least one matching font descriptor is determined. The local font corresponding to the matching font descriptor is deemed similar to the original font. The destination computing device controls presentations of the document using the similar local font. Computation of font descriptors can be outsourced to a remote location.
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94.发明授权公开(公告)号：US10769764B2
公开(公告)日：2020-09-08
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：US20200151503A1
公开(公告)日：2020-05-14
申请号：US16184779
申请日：2018-11-08
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Hailin Jin , Yang Liu
Abstract: In implementations of recognizing text in images, text recognition systems are trained using noisy images that have nuisance factors applied, and corresponding clean images (e.g., without nuisance factors). Clean images serve as supervision at both feature and pixel levels, so that text recognition systems are trained to be feature invariant (e.g., by requiring features extracted from a noisy image to match features extracted from a clean image), and feature complete (e.g., by requiring that features extracted from a noisy image be sufficient to generate a clean image). Accordingly, text recognition systems generalize to text not included in training images, and are robust to nuisance factors. Furthermore, since clean images are provided as supervision at feature and pixel levels, training requires fewer training images than text recognition systems that are not trained with a supervisory clean image, thus saving time and resources.
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Patent Agency Ranking
公开(公告)号：US10592787B2
公开(公告)日：2020-03-17
申请号：US15807028
申请日：2017-11-08
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , Hailin Jin
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and adversarial training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system adversarial trains a font recognition neural network by minimizing font classification loss while at the same time maximizing glyph classification loss. By employing an adversarially trained font classification neural network, the font recognition system can improve overall font recognition by removing the negative side effects from diverse glyph content.
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公开(公告)号：US10592590B2
公开(公告)日：2020-03-17
申请号：US15229108
申请日：2016-08-04
Applicant: ADOBE INC.
Inventor： I-Ming Pao , Alan Lee Erickson , Yuyan Song , Seth Shaw , Hailin Jin , Zhaowen Wang
Abstract: Embodiments of the present invention are directed at providing a font similarity preview for non-resident fonts. In one embodiment, a font is selected on a computing device. In response to the selection of the font, a pre-computed font list is checked to determine what fonts are similar to the selected font. In response to a determination that similar fonts are not local to the computing device, a non-resident font list is sent to a font vendor. The font vendor sends back previews of the non-resident fonts based on entitlement information of a user. Further, a full non-resident font can be synced to the computing device. Other embodiments may be described and/or claimed.
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公开(公告)号：US20200034671A1
公开(公告)日：2020-01-30
申请号：US16590121
申请日：2019-10-01
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
IPC: G06K9/68 , G06K9/46 , G06N3/04 , G06K9/62 , G06K9/00 , G06K9/66 , G06T3/40 , G06K9/52 , G06T7/60
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：US10515296B2
公开(公告)日：2019-12-24
申请号：US15812548
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , I-Ming Pao , Hailin Jin
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system trains a hybrid font recognition neural network that includes two or more font recognition neural networks and a weight prediction neural network. The hybrid font recognition neural network determines and generates classification weights based on which font recognition neural network within the hybrid font recognition neural network is best suited to classify the font in an input text image. By employing a hybrid trained font classification neural network, the font recognition system can improve overall font recognition as well as remove the negative side effects from diverse glyph content.
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公开(公告)号：US20190325277A1
公开(公告)日：2019-10-24
申请号：US16502608
申请日：2019-07-03
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
Abstract: Font replacement based on visual similarity is described. In one or more embodiments, a font descriptor includes multiple font features derived from a visual appearance of a font by a font visual similarity model. The font visual similarity model can be trained using a machine learning system that recognizes similarity between visual appearances of two different fonts. A source computing device embeds a font descriptor in a document, which is transmitted to a destination computing device. The destination compares the embedded font descriptor to font descriptors corresponding to local fonts. Based on distances between the embedded and the local font descriptors, at least one matching font descriptor is determined. The local font corresponding to the matching font descriptor is deemed similar to the original font. The destination computing device controls presentations of the document using the similar local font. Computation of font descriptors can be outsourced to a remote location.
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