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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：US20240169622A1
公开(公告)日：2024-05-23
申请号：US18057851
申请日：2022-11-22
Applicant: ADOBE INC.
Inventor： Shaoan Xie , Zhifei Zhang , Zhe Lin , Tobias Hinz
CPC classification number: G06T11/60 , G06T7/11 , G06T11/001 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods for multi-modal image editing are provided. In one aspect, a system and method for multi-modal image editing includes identifying an image, a prompt identifying an element to be added to the image, and a mask indicating a first region of the image for depicting the element. The system then generates a partially noisy image map that includes noise in the first region and image features from the image in a second region outside the first region. A diffusion model generates a composite image map based on the partially noisy image map and the prompt. In some cases, the composite image map includes the target element in the first region that corresponds to the mask.
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3.发明公开公开(公告)号：US20230325996A1
公开(公告)日：2023-10-12
申请号：US18167690
申请日：2023-02-10
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Jianming Zhang , Scott Cohen , Zhe Lin
IPC: G06T5/50 , G06T3/40 , G06V10/60 , G06F3/04842
CPC classification number: G06T5/50 , G06T3/40 , G06V10/60 , G06F3/04842 , G06T2207/20101 , G06T2207/20104 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generates composite images via auto-compositing features. For example, in one or more embodiments, the disclosed systems determine a background image and a foreground object image for use in generating a composite image. The disclosed systems further provide, for display within a graphical user interface of a client device, at least one selectable option for executing an auto-composite model for the composite image, the auto-composite model comprising at least one of a scale prediction model, a harmonization model, or a shadow generation model. The disclosed systems detect, via the graphical user interface, a user selection of the at least one selectable option and generate, in response to detecting the user selection, the composite image by executing the auto-composite model using the background image and the foreground object image.
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4.发明公开公开(公告)号：US20230325991A1
公开(公告)日：2023-10-12
申请号：US17658770
申请日：2022-04-11
Applicant: Adobe Inc.
Inventor： Zhe Lin , Sijie Zhu , Jason Wen Yong Kuen , Scott Cohen , Zhifei Zhang
CPC classification number: G06T5/50 , G06T7/194 , G06T5/002 , G06T3/60 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.
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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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Patent Agency Ranking
公开(公告)号：US20250022099A1
公开(公告)日：2025-01-16
申请号：US18351838
申请日：2023-07-13
Applicant: ADOBE INC.
Inventor： Yizhi Song , Zhifei Zhang , Zhe Lin , Scott Cohen , Brian Lynn Price , Jianming Zhang , Soo Ye Kim
Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.
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公开(公告)号：US12154196B2
公开(公告)日：2024-11-26
申请号：US17810392
申请日：2022-07-01
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Scott Cohen , Darshan Prasad , Zhihong Ding
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.
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公开(公告)号：US11875435B2
公开(公告)日：2024-01-16
申请号：US17499611
申请日：2021-10-12
Applicant: Adobe Inc.
Inventor： Chinthala Pradyumna Reddy , Zhifei Zhang , Matthew Fisher , Hailin Jin , Zhaowen Wang , Niloy J Mitra
CPC classification number: G06T11/203 , G06T3/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.
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9.发明公开公开(公告)号：US20240004924A1
公开(公告)日：2024-01-04
申请号：US17809781
申请日：2022-06-29
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Zhihong Ding , Scott Cohen , Darshan Prasad
IPC: G06F16/538 , G06F16/532 , G06T7/11 , G06T5/50 , G06F16/583
CPC classification number: G06F16/538 , G06F16/532 , G06F16/5838 , G06T5/50 , G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.
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公开(公告)号：US20230419571A1
公开(公告)日：2023-12-28
申请号：US17809494
申请日：2022-06-28
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Scott Cohen , Kevin Gary Smith
IPC: G06T11/60 , G06T11/20 , G06F16/532
CPC classification number: G06T11/60 , G06T11/203 , G06F16/532 , G06T2200/24 , G06T2207/20084 , G06F3/0482
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.
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