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公开(公告)号：US12299844B2
公开(公告)日：2025-05-13
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
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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2.发明公开公开(公告)号：US20230326028A1
公开(公告)日：2023-10-12
申请号：US17658873
申请日：2022-04-12
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Soo Ye Kim , Simon Niklaus , Yifei Fan , Su Chen , Zhe Lin
CPC classification number: G06T7/11 , G06T2207/20084 , G06T7/50 , G06T7/215
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate refined depth maps of digital images utilizing digital segmentation masks. In particular, in one or more embodiments, the disclosed systems generate a depth map for a digital image utilizing a depth estimation machine learning model, determine a digital segmentation mask for the digital image, and generate a refined depth map from the depth map and the digital segmentation mask utilizing a depth refinement machine learning model. In some embodiments, the disclosed systems generate first and second intermediate depth maps using the digital segmentation mask and an inverse digital segmentation mask and merger the first and second intermediate depth maps to generate the refined depth map.
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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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4.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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公开(公告)号：US11935217B2
公开(公告)日：2024-03-19
申请号：US17200338
申请日：2021-03-12
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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Patent Agency Ranking
公开(公告)号：US11443481B1
公开(公告)日：2022-09-13
申请号：US17186522
申请日：2021-02-26
Applicant: Adobe Inc.
Inventor： Wei Yin , Jianming Zhang , Oliver Wang , Simon Niklaus , Mai Long , Su Chen
Abstract: This disclosure describes implementations of a three-dimensional (3D) scene recovery system that reconstructs a 3D scene representation of a scene portrayed in a single digital image. For instance, the 3D scene recovery system trains and utilizes a 3D point cloud model to recover accurate intrinsic camera parameters from a depth map of the digital image. Additionally, the 3D point cloud model may include multiple neural networks that target specific intrinsic camera parameters. For example, the 3D point cloud model may include a depth 3D point cloud neural network that recovers the depth shift as well as include a focal length 3D point cloud neural network that recovers the camera focal length. Further, the 3D scene recovery system may utilize the recovered intrinsic camera parameters to transform the single digital image into an accurate and realistic 3D scene representation, such as a 3D point cloud.
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公开(公告)号：US20240281978A1
公开(公告)日：2024-08-22
申请号：US18170336
申请日：2023-02-16
Applicant: Adobe Inc.
Inventor： Jingyuan Liu , Qing Liu , Jimei Yang , Yuhong Wu , Su Chen
CPC classification number: G06T7/11 , G06V10/267 , G06V10/7715 , G06V10/82 , G06V20/70 , G06T2207/20021 , G06T2207/20084
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating segmentation masks for a digital visual media item. In particular, in one or more embodiments, the disclosed systems generate, utilizing a neural network encoder, high-level features of a digital visual media item. Further, the disclosed systems generate, utilizing the neural network encoder, low-level features of the digital visual media item. In some implementations, the disclosed systems generate, utilizing a neural network decoder, an initial segmentation mask of the digital visual media item from the low-level features. Moreover, the disclosed systems generate, utilizing the neural network decoder, a refined segmentation mask of the digital visual media item from the initial segmentation mask and the high-level features.
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公开(公告)号：US20230128792A1
公开(公告)日：2023-04-27
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：US20220284613A1
公开(公告)日：2022-09-08
申请号：US17186436
申请日：2021-02-26
Applicant: Adobe Inc.
Inventor： Wei Yin , Jianming Zhang , Oliver Wang , Simon Niklaus , Mai Long , Su Chen
Abstract: This disclosure describes one or more implementations of a depth prediction system that generates accurate depth images from single input digital images. In one or more implementations, the depth prediction system enforces different sets of loss functions across mix-data sources to generate a multi-branch architecture depth prediction model. For instance, in one or more implementations, the depth prediction model utilizes different data sources having different granularities of ground truth depth data to robustly train a depth prediction model. Further, given the different ground truth depth data granularities from the different data sources, the depth prediction model enforces different combinations of loss functions including an image-level normalized regression loss function and/or a pair-wise normal loss among other loss functions.
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公开(公告)号：US20220198671A1
公开(公告)日：2022-06-23
申请号：US17126986
申请日：2020-12-18
Applicant: Adobe Inc.
Inventor： Brian Price , Su Chen , Shuo Yang
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a deep neural network to process object user indicators and an initial object segmentation from a digital image to efficiently and flexibly generate accurate object segmentations. In particular, the disclosed systems can determine an initial object segmentation for the digital image (e.g., utilizing an object segmentation model or interactive selection processes). In addition, the disclosed systems can identify an object user indicator for correcting the initial object segmentation and generate a distance map reflecting distances between pixels of the digital image and the object user indicator. The disclosed systems can generate an image-interaction-segmentation triplet by combining the digital image, the initial object segmentation, and the distance map. By processing the image-interaction-segmentation triplet utilizing the segmentation neural network, the disclosed systems can provide an updated object segmentation for display to a client device.
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