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11.发明授权公开(公告)号：US12254170B2
公开(公告)日：2025-03-18
申请号：US18313529
申请日：2023-05-08
Applicant: Adobe Inc.
IPC: G06Q30/02 , G06F3/04845 , G06F40/106 , G06F40/284 , G06T7/60
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a design language model and a generative language model to generate digital design documents with design variations. In particular embodiments, the disclosed systems implement the design language model to tokenize the design of a document into a sequence of language tokens. For example, the disclosed systems tokenize visual elements and a layout of the document—in addition to optional user-added content. The generative language model utilizes the sequence of language tokens to predict a next language token representing a suggested design variation. Based on the predicted language token, the disclosed systems generate a modified digital design document visually portraying the suggested design variation. Further, in one or more embodiments, the disclosed systems perform iterative refinements to the modified digital design document.
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12.发明公开公开(公告)号：US20230305690A1
公开(公告)日：2023-09-28
申请号：US18313529
申请日：2023-05-08
Applicant: Adobe Inc.
IPC: G06F3/04845 , G06F40/106 , G06F40/284 , G06T7/60
CPC classification number: G06F3/04845 , G06F40/106 , G06F40/284 , G06T7/60
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a design language model and a generative language model to generate digital design documents with design variations. In particular embodiments, the disclosed systems implement the design language model to tokenize the design of a document into a sequence of language tokens. For example, the disclosed systems tokenize visual elements and a layout of the document—in addition to optional user-added content. The generative language model utilizes the sequence of language tokens to predict a next language token representing a suggested design variation. Based on the predicted language token, the disclosed systems generate a modified digital design document visually portraying the suggested design variation. Further, in one or more embodiments, the disclosed systems perform iterative refinements to the modified digital design document.
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公开(公告)号：US20220343470A1
公开(公告)日：2022-10-27
申请号：US17859435
申请日：2022-07-07
Applicant: Adobe Inc.
Inventor： Ionut Mironica , Oscar Bolaños , Andreea Birhala
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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公开(公告)号：US11468658B2
公开(公告)日：2022-10-11
申请号：US16928881
申请日：2020-07-14
Applicant: Adobe Inc.
Inventor： Ionut Mironica
IPC: G06V10/20 , G06F40/109 , G06N3/08 , G06V10/44 , G06V30/40
Abstract: This disclosure involves automatically generating a typographical image using an image and a text document. Aspects of the present disclosure include detecting a region of interest from the image and generating an object template from the detected region of interest. The object template defines the areas of the image, in which words of the text document are inserted. A text rendering protocol is executed to iteratively insert the words of the text document into the available locations of the object template. The typographical image is generated by rendering each word of the text document onto the available location assigned to the word.
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公开(公告)号：US20220019830A1
公开(公告)日：2022-01-20
申请号：US16928881
申请日：2020-07-14
Applicant: Adobe Inc.
Inventor： Ionut Mironica
IPC: G06K9/32 , G06K9/46 , G06K9/00 , G06N3/08 , G06F40/109
Abstract: This disclosure involves automatically generating a typographical image using an image and a text document. Aspects of the present disclosure include detecting a region of interest from the image and generating an object template from the detected region of interest. The object template defines the areas of the image, in which words of the text document are inserted. A text rendering protocol is executed to iteratively insert the words of the text document into the available locations of the object template. The typographical image is generated by rendering each word of the text document onto the available location assigned to the word.
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Patent Agency Ranking
公开(公告)号：US20250111137A1
公开(公告)日：2025-04-03
申请号：US18477978
申请日：2023-09-29
Applicant: ADOBE INC.
IPC: G06F40/186 , G06F40/126 , G06F40/30 , G06F40/40
Abstract: Systems and methods for generating full designs from text include retrieving a plurality of document templates based on a design prompt, and filtering the document templates based on an image prompt. A document template is then selected based on the filtering, and a document is generated based on the document template and the image prompt. Embodiments are further configured to generate content from one or more of the prompts, where the content is included in the final design document.
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公开(公告)号：US20250069204A1
公开(公告)日：2025-02-27
申请号：US18944363
申请日：2024-11-12
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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公开(公告)号：US20240420394A1
公开(公告)日：2024-12-19
申请号：US18334610
申请日：2023-06-14
Applicant: ADOBE INC.
Inventor： Ionut Mironica , Marian Lupascu , Alexandru Vasile Costin , Cristian Catalin Buzoiu , Aliakbar Darabi
Abstract: Systems and methods are provided for image editing, and more particularly, for harmonizing background images with text. Embodiments of the present disclosure obtain an image including text and a region overlapping the text. In some aspects, the text includes a first color. Embodiments then select a second color that contrasts with the first color, and generate a modified image including the text and a modified region using a machine learning model that takes the image and the second color as input. The modified image is generated conditionally, so as to include the second color in a region corresponding to the text.
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19.发明授权公开(公告)号：US11887277B2
公开(公告)日：2024-01-30
申请号：US17182510
申请日：2021-02-23
Applicant: Adobe Inc.
Inventor： Ionut Mironica
CPC classification number: G06T5/001 , G06N3/045 , G06N3/088 , G06T9/00 , G06T2207/20081 , G06T2207/20084
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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20.发明授权公开(公告)号：US11644961B1
公开(公告)日：2023-05-09
申请号：US17652044
申请日：2022-02-22
Applicant: Adobe Inc.
IPC: G06Q30/02 , G06F3/04845 , G06F40/106 , G06F40/284 , G06T7/60
CPC classification number: G06F3/04845 , G06F40/106 , G06F40/284 , G06T7/60
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a design language model and a generative language model to generate digital design documents with design variations. In particular embodiments, the disclosed systems implement the design language model to tokenize the design of a document into a sequence of language tokens. For example, the disclosed systems tokenize visual elements and a layout of the document—in addition to optional user-added content. The generative language model utilizes the sequence of language tokens to predict a next language token representing a suggested design variation. Based on the predicted language token, the disclosed systems generate a modified digital design document visually portraying the suggested design variation. Further, in one or more embodiments, the disclosed systems perform iterative refinements to the modified digital design document.
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