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公开(公告)号：US12288279B2
公开(公告)日：2025-04-29
申请号：US18058538
申请日：2022-11-23
Applicant: Adobe Inc.
Inventor： Jonathan Brandt , Scott Cohen , Zhe Lin , Zhihong Ding , Darshan Prasad , Matthew Joss , Celso Gomes , Jianming Zhang , Olena Soroka , Klaas Stoeckmann , Michael Zimmermann , Thomas Muehrke
IPC: G06T11/60 , G06F3/048 , G06F3/04842 , G06F3/04845 , G06T11/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate utilizing a segmentation neural network, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.
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公开(公告)号：US12272127B2
公开(公告)日：2025-04-08
申请号：US17589114
申请日：2022-01-31
Applicant: Adobe Inc.
Inventor： Jason Wen Yong Kuen , Su Chen , Scott Cohen , Zhe Lin , Zijun Wei , Jianming Zhang
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.
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公开(公告)号：US20250086849A1
公开(公告)日：2025-03-13
申请号：US18463333
申请日：2023-09-08
Applicant: ADOBE INC.
Inventor： Yu Zeng , Zhe Lin , Jianming Zhang , Qing Liu , Jason Wen Yong Kuen , John Philip Collomosse
IPC: G06T11/00 , G06F40/295 , G06F40/30 , G06V10/774 , G06V10/776 , G06V20/70
Abstract: Embodiments of the present disclosure include obtaining a text prompt describing an element, layout information indicating a target region for the element, and a precision level corresponding to the element. Some embodiments generate a text feature pyramid based on the text prompt, the layout information, and the precision level, wherein the text feature pyramid comprises a plurality of text feature maps at a plurality of scales, respectively. Then, an image is generated based on the text feature pyramid. In some cases, the image includes an object corresponding to the element of the text prompt at the target region. Additionally, a shape of the object corresponds to a shape of the target region based on the precision level.
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94.发明授权公开(公告)号：US12198224B2
公开(公告)日：2025-01-14
申请号：US17651075
申请日：2022-02-15
Applicant: ADOBE INC.
Inventor： Xin Yuan , Zhe Lin , Jason Wen Yong Kuen , Jianming Zhang , John Philip Collomosse
Abstract: Systems and methods for image generation are described. Embodiments of the present disclosure receive a text phrase that describes a target image to be generated; generate text features based on the text phrase; retrieve a search image based on the text phrase; and generate the target image using an image generation network based on the text features and the search image.
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公开(公告)号：US12169895B2
公开(公告)日：2024-12-17
申请号：US17502782
申请日：2021-10-15
Applicant: Adobe Inc.
Inventor： Yifan Liu , Jianming Zhang , He Zhang , Elya Shechtman , Zhe Lin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a height map for a digital object portrayed in a digital image and further utilizes the height map to generate a shadow for the digital object. Indeed, in one or more embodiments, the disclosed systems generate (e.g., utilizing a neural network) a height map that indicates the pixels heights for pixels of a digital object portrayed in a digital image. The disclosed systems utilize the pixel heights, along with lighting information for the digital image, to determine how the pixels of the digital image project to create a shadow for the digital object. Further, in some implementations, the disclosed systems utilize the determined shadow projections to generate (e.g., utilizing another neural network) a soft shadow for the digital object. Accordingly, in some cases, the disclosed systems modify the digital image to include the shadow.
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Patent Agency Ranking
公开(公告)号：US12125227B2
公开(公告)日：2024-10-22
申请号：US17656605
申请日：2022-03-25
Applicant: Adobe Inc.
Inventor： Jianming Zhang
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and/or implementing machine learning models utilizing compressed log scene measurement maps. For example, the disclosed system generates compressed log scene measurement maps by converting scene measurement maps to compressed log scene measurement maps by applying a logarithmic function. In particular, the disclosed system uses scene measurement distribution metrics from a digital image to determine a base for the logarithmic function. In this way, the compressed log scene measurement maps normalize ranges within a digital image and accurately differentiates between scene elements objects at a variety of depths. Moreover, for training, the disclosed system generates a predicted scene measurement map via a machine learning model and compares the predicted scene measurement map with a compressed log ground truth map. By doing so, the disclosed system trains the machine learning model to generate accurate compressed log depth maps.
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97.发明授权公开(公告)号：US12008734B2
公开(公告)日：2024-06-11
申请号：US17823364
申请日：2022-08-30
Applicant: Adobe Inc.
Inventor： Jianming Zhang
CPC classification number: G06T5/40 , G06T5/70 , G06T7/11 , G06T7/12 , G06T7/194 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize color density estimation in a blended boundary region of a digital image to generate an image mask. For example, the disclosed system extracts a foreground region, a background region, and a blended boundary region from a digital image. The disclosed system determines a color histogram—within a color space selected utilizing the foreground region and the background region—for a portion of the background region along an edge of the blended boundary region. Additionally, the disclosed system generates a color density map for the blended boundary region by comparing colors in the blended boundary region to colors in the color histogram of the background band. The disclosed system then generates a final mask for the digital image based on the color density map.
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98.发明公开公开(公告)号：US20240185393A1
公开(公告)日：2024-06-06
申请号：US18440248
申请日：2024-02-13
Applicant: Adobe Inc.
Inventor： He Zhang , Yifan Jiang , Yilin Wang , Jianming Zhang , Kalyan Sunkavalli , Sarah Kong , Su Chen , Sohrab Amirghodsi , Zhe Lin
CPC classification number: G06T5/50 , G06N3/04 , G06N3/08 , G06T7/194 , G06T11/001 , G06T11/60 , G06T2207/20081 , G06T2207/20084 , G06T2207/20092 , G06T2207/20132 , G06T2207/20212
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.
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99.发明公开公开(公告)号：US20240169631A1
公开(公告)日：2024-05-23
申请号：US18532485
申请日：2023-12-07
Applicant: Adobe Inc.
Inventor： Soo Ye Kim , Zhe Lin , Scott Cohen , Jianming Zhang , Luis Figueroa , Zhihong Ding
IPC: G06T11/60 , G06F3/0481 , G06F3/04845 , G06F3/0486 , G06T5/00 , G06T11/00
CPC classification number: G06T11/60 , G06F3/0481 , G06F3/04845 , G06F3/0486 , G06T5/002 , G06T5/005 , G06T11/001 , G06T2200/24 , G06T2207/20092 , G06T2207/20212
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.
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公开(公告)号：US20240135561A1
公开(公告)日：2024-04-25
申请号：US18320714
申请日：2023-05-19
Applicant: Adobe Inc.
Inventor： Zhihong Ding , Scott Cohen , Matthew Joss , Jianming Zhang , Darshan Prasad , Celso Gomes , Jonathan Brandt
CPC classification number: G06T7/50 , G06T5/005 , G06V10/26 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implement depth-aware object move operations for digital image editing. For instance, in some embodiments, the disclosed systems determine a first object depth for a first object portrayed within a digital image and a second object depth for a second object portrayed within the digital image. Additionally, the disclosed systems move the first object to create an overlap area between the first object and the second object within the digital image. Based on the first object depth and the second object depth, the disclosed systems modify the digital image to occlude the first object or the second object within the overlap area.
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