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公开(公告)号：US20200013205A1
公开(公告)日：2020-01-09
申请号：US16028075
申请日：2018-07-05
Applicant: Adobe Inc.
Inventor： Mridul Kavidayal , Vineet Batra , Jingwan Lu , Ankit Phogat
Abstract: There is disclosed a system and method for colorizing vector graphic objects in a digital medium environment. The system comprises a processing unit and a deep neural network of the processing unit, in which the deep neural network includes a generator. The processing unit receives a non-colorized vector image and converts the non-colorized vector image to a non-colorized raster image. The deep neural network generates a colorized raster image from the non-colorized raster image. The generator processes the non-colorized raster image using an extended number of convolutional layers and residual blocks to add skip connections between at least two of the convolutional layers. The processing unit converts the colorized raster image to a colorized vector image.
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公开(公告)号：US20190304141A1
公开(公告)日：2019-10-03
申请号：US16448127
申请日：2019-06-21
Applicant: Adobe Inc.
Inventor： Maria Shugrina , Stephen J. DiVerdi , Jingwan Lu
IPC: G06T11/00 , G06F3/0488
Abstract: An interactive palette interface includes a color picker for digital paint applications. A user can create, modify and select colors for creating digital artwork using the interactive palette interface. The interactive palette interface includes a mixing dish in which colors can be added, removed and rearranged to blend together to create gradients and gamuts. The mixing dish is a digital simulation of a physical palette on which an artist adds and mixes various colors of paint before applying the paint to the artwork. Color blobs, which are logical groups of pixels in the mixing dish, can be spatially rearranged and scaled by a user to create and explore different combinations of colors. The color, position and size of each blob influences the color of other pixels in the mixing dish. Edits to the mixing dish are non-destructive, and an infinite history of color combinations is preserved.
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公开(公告)号：US20190130894A1
公开(公告)日：2019-05-02
申请号：US15796292
申请日：2017-10-27
Applicant: Adobe Inc. , The Trustees of Princeton University
Inventor： Zeyu Jin , Gautham J. Mysore , Stephen DiVerdi , Jingwan Lu , Adam Finkelstein
CPC classification number: G10L13/08 , G06F17/24 , G10L13/00 , G10L13/04 , G10L13/06 , G10L13/07 , G10L15/02 , G10L21/00 , G10L2021/0135 , G11B27/022
Abstract: Systems and techniques are disclosed for synthesizing a new word or short phrase such that it blends seamlessly in the context of insertion or replacement in an existing narration. In one such embodiment, a text-to-speech synthesizer is utilized to say the word or phrase in a generic voice. Voice conversion is then performed on the generic voice to convert it into a voice that matches the narration. An editor and interface are described that support fully automatic synthesis, selection among a candidate set of alternative pronunciations, fine control over edit placements and pitch profiles, and guidance by the editors own voice.
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公开(公告)号：US12249132B2
公开(公告)日：2025-03-11
申请号：US17815451
申请日：2022-07-27
Applicant: Adobe Inc.
Inventor： Yijun Li , Nicholas Kolkin , Jingwan Lu , Elya Shechtman
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.
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公开(公告)号：US20250069203A1
公开(公告)日：2025-02-27
申请号：US18454850
申请日：2023-08-24
Applicant: ADOBE INC.
Inventor： Yuqian Zhou , Krishna Kumar Singh , Benjamin Delarre , Zhe Lin , Jingwan Lu , Taesung Park , Sohrab Amirghodsi , Elya Shechtman
Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation are described. An embodiment of the present disclosure includes obtaining an input image, an inpainting mask, and a plurality of content preservation values corresponding to different regions of the inpainting mask, and identifying a plurality of mask bands of the inpainting mask based on the plurality of content preservation values. An image generation model generates an output image based on the input image and the inpainting mask. The output image is generated in a plurality of phases. Each of the plurality of phases uses a corresponding mask band of the plurality of mask bands as an input.
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Patent Agency Ranking
公开(公告)号：US12204610B2
公开(公告)日：2025-01-21
申请号：US17650967
申请日：2022-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Jingwan Lu , Scott Cohen , Jianming Zhang , Ning Xu , Elya Shechtman , Connelly Barnes , Sohrab Amirghodsi
IPC: G06K9/00 , G06F18/214 , G06N3/08 , G06T5/77 , G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization.
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公开(公告)号：US20240404013A1
公开(公告)日：2024-12-05
申请号：US18515378
申请日：2023-11-21
Applicant: ADOBE INC.
Inventor： Yuqian Zhou , Krishna Kumar Singh , Zhe Lin , Qing Liu , Zhifei Zhang , Sohrab Amirghodsi , Elya Shechtman , Jingwan Lu
Abstract: Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.
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公开(公告)号：US12159413B2
公开(公告)日：2024-12-03
申请号：US17693618
申请日：2022-03-14
Applicant: Adobe Inc.
Inventor： Gaurav Parmar , Krishna Kumar Singh , Yijun Li , Richard Zhang , Jingwan Lu
IPC: G06T7/11 , G06T3/4046 , G06T11/00
Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.
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公开(公告)号：US20240338799A1
公开(公告)日：2024-10-10
申请号：US18178212
申请日：2023-03-03
Applicant: Adobe Inc.
Inventor： Yijun Li , Richard Zhang , Krishna Kumar Singh , Jingwan Lu , Gaurav Parmar , Jun-Yan Zhu
IPC: G06T5/00 , G06F40/126 , G06T5/50
CPC classification number: G06T5/70 , G06F40/126 , G06T5/50 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.
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30.发明公开公开(公告)号：US20240331236A1
公开(公告)日：2024-10-03
申请号：US18178194
申请日：2023-03-03
Applicant: Adobe Inc.
Inventor： Yijun Li , Richard Zhang , Krishna Kumar Singh , Jingwan Lu , Gaurav Parmar , Jun-Yan Zhu
CPC classification number: G06T11/60 , G06T5/70 , G06T9/00 , G06V10/761 , G06V10/82 , G06V20/70 , G06T2207/20182
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.
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