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公开(公告)号：US20240127577A1
公开(公告)日：2024-04-18
申请号：US17965291
申请日：2022-10-13
Applicant: Adobe Inc.
CPC classification number: G06V10/761 , G06T11/60
Abstract: In implementations of systems for generating templates using structure-based matching, a computing device implements a template system to receive input data describing a set of digital design elements. The template system represents the input data as a sentence in a design structure language that describes structural relationships between design elements included in the set of digital design elements. An input template embedding is generated based on the sentence in the design structure language. The template system generates a digital template that includes the set of digital design elements for display in a user interface based on the input template embedding.
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公开(公告)号：US20210342972A1
公开(公告)日：2021-11-04
申请号：US16862424
申请日：2020-04-29
Applicant: Adobe Inc.
Inventor： Ionut Mironica , Andreea Bîrhala
IPC: G06T3/40 , G06T7/00 , G06F3/0482 , G06F3/0484
Abstract: Techniques and systems are described for automatic content-aware collages. Collage templates are generated based on generated set of initial points. Salient regions are determined within digital images, and the salient regions are matched with cells of a collage template. Chrominance of digital images may be mediated to provide a cohesive color scheme among the digital images, and geometric parameters of digital images may be generated to optimize visible salient regions within cells of the template. A collage is generated incorporating the digital images in corresponding cells of the template.
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公开(公告)号：US20240020810A1
公开(公告)日：2024-01-18
申请号：US18474588
申请日：2023-09-26
Applicant: Adobe Inc.
Inventor： Yijun Li , Ionut Mironica
CPC classification number: G06T5/50 , G06T11/001 , G06T5/002 , G06T7/50 , G06N3/04 , G06T2207/20084 , G06T2200/24 , G06T2207/20221 , G06T2207/20081
Abstract: Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.
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公开(公告)号：US11763430B2
公开(公告)日：2023-09-19
申请号：US17859435
申请日：2022-07-07
Applicant: Adobe Inc.
Inventor： Ionut Mironica , Oscar Bolaños , Andreea Birhala
CPC classification number: G06T5/005 , G06F18/213 , G06T5/50 , G06T7/0002 , G06T7/337 , G06V10/30 , G06V10/82 , G06T2200/24 , G06T2207/20081 , G06T2207/20084 , G06T2207/30168
Abstract: In implementations of correcting dust and scratch artifacts in digital images, an artifact correction system receives a digital image that depicts a scene and includes a dust or scratch artifact. The artifact correction system generates, with a generator of a generative adversarial neural network (GAN), a feature map from the digital image that represents features of the dust or scratch artifact and features of the scene. A training system can train the generator adversarially to reduce visibility of dust and scratch artifacts in digital images against a discriminator, and train the discriminator to distinguish between reconstructed digital images generated by the generator and real-world digital images. The artifact correction system generates, from the feature map and with the generator, a reconstructed digital image that depicts the scene of the digital image and reduces visibility of the dust or scratch artifact of the digital image.
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5.发明公开公开(公告)号：US20230267652A1
公开(公告)日：2023-08-24
申请号：US17652390
申请日：2022-02-24
Applicant: Adobe Inc.
Inventor： Marian Lupascu , Ryan Murdock , Ionut Mironica , Yijun Li
CPC classification number: G06T11/00 , G06T3/4046 , G06T3/4053 , G06T2210/36 , G06T2210/22
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize an iterative neural network framework for generating artistic visual content. For instance, in one or more embodiments, the disclosed systems receive style parameters in the form a style image and/or a text prompt. In some cases, the disclosed systems further receive a content image having content to include in the artistic visual content. Accordingly, in one or more embodiments, the disclosed systems utilize a neural network to generate the artistic visual content by iteratively generating an image, comparing the image to the style parameters, and updating parameters for generating the next image based on the comparison. In some instances, the disclosed systems incorporate a superzoom network into the neural network for increasing the resolution of the final image and adding art details that are associated with a physical art medium (e.g., brush strokes).
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Patent Agency Ranking
6.发明申请公开(公告)号：US20230055204A1
公开(公告)日：2023-02-23
申请号：US17405207
申请日：2021-08-18
Applicant: Adobe Inc.
Inventor： Adrian-Stefan Ungureanu , Ionut Mironica , Richard Zhang
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize one or more stages of a two-stage image colorization neural network to colorize or re-colorize digital images. In one or more embodiments, the disclosed system generates a color digital image from a grayscale digital image by utilizing a colorization neural network. Additionally, the disclosed system receives one or more inputs indicating local hints comprising one or more color selections to apply to one or more objects of the color digital image. The disclosed system then utilizes a re-colorization neural network to generate a modified digital image from the color digital image by modifying one or more colors of the object(s) based on the luminance channel, color channels, and selected color(s).
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公开(公告)号：US20250148670A1
公开(公告)日：2025-05-08
申请号：US18502778
申请日：2023-11-06
Applicant: Adobe Inc.
Inventor： Marian Lupascu , Vlad-Constantin Lungu-Stan , Ionut Mironica , George Bogdan Avram
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital designs utilizing a diffusion neural network to preserve readability and design composition while modifying image content background images and design assets. In some embodiments, the disclosed systems access a text prompt defining visual attributes of a digital design. Furthermore, the disclosed systems generate a modified text prompt by replacing chromatic information within the text prompt. Additionally, the disclosed systems determine an adaptive strength for a diffusion neural network from the text prompt. Also, the disclosed systems generate a modified digital design utilizing the diffusion neural network to process the modified text prompt according to the adaptive strength.
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公开(公告)号：US12175641B2
公开(公告)日：2024-12-24
申请号：US17338949
申请日：2021-06-04
Applicant: Adobe Inc.
Inventor： Ionut Mironica , Yijun Li
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.
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9.发明授权公开(公告)号：US12118647B2
公开(公告)日：2024-10-15
申请号：US17405207
申请日：2021-08-18
Applicant: Adobe Inc.
Inventor： Adrian-Stefan Ungureanu , Ionut Mironica , Richard Zhang
CPC classification number: G06T11/001 , G06N3/04
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize one or more stages of a two-stage image colorization neural network to colorize or re-colorize digital images. In one or more embodiments, the disclosed system generates a color digital image from a grayscale digital image by utilizing a colorization neural network. Additionally, the disclosed system receives one or more inputs indicating local hints comprising one or more color selections to apply to one or more objects of the color digital image. The disclosed system then utilizes a re-colorization neural network to generate a modified digital image from the color digital image by modifying one or more colors of the object(s) based on the luminance channel, color channels, and selected color(s).
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10.发明申请公开(公告)号：US20220270209A1
公开(公告)日：2022-08-25
申请号：US17182510
申请日：2021-02-23
Applicant: Adobe Inc.
Inventor： Ionut Mironica
Abstract: The present disclosure relates to an image artifact removal system that improves digital images by removing complex artifacts caused by image compression. For example, in various implementations, the image artifact removal system builds a generative adversarial network that includes a generator neural network and a discriminator neural network. In addition, the image artifact removal system trains the generator neural network to reduce and eliminate compression artifacts from the image by synthesizing or retouching the compressed digital image. Further, in various implementations, the image artifact removal system utilizes dilated attention residual layers in the generator neural network to accurately remove compression artifacts from digital images of different sizes and/or having different compression ratios.
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