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公开(公告)号：US20230306637A1
公开(公告)日：2023-09-28
申请号：US17656796
申请日：2022-03-28
Applicant: ADOBE INC.
Inventor： Jianming ZHANG , Linyi JIN , Kevin MATZEN , Oliver WANG , Yannick HOLD-GEOFFROY
CPC classification number: G06T7/80 , G06T9/002 , G06T11/00 , G06N3/0454 , G06T2207/20081 , G06T2207/20084 , G06V10/764
Abstract: Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.
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公开(公告)号：US20220366546A1
公开(公告)日：2022-11-17
申请号：US17812639
申请日：2022-07-14
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.
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公开(公告)号：US20210342984A1
公开(公告)日：2021-11-04
申请号：US16864388
申请日：2020-05-01
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of high-resolution images using guided upsampling during image inpainting. For instance, an image inpainting system can apply guided upsampling to an inpainted image result to enable generation of a high-resolution inpainting result from a lower-resolution image that has undergone inpainting. To allow for guided upsampling during image inpainting, one or more neural networks can be used. For instance, a low-resolution result neural network (e.g., comprised of an encoder and a decoder) and a high-resolution input neural network (e.g., comprised of an encoder and a decoder). The image inpainting system can use such networks to generate a high-resolution inpainting image result that fills the hole, region, and/or portion of the image.
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公开(公告)号：US20210082118A1
公开(公告)日：2021-03-18
申请号：US16574513
申请日：2019-09-18
Applicant: ADOBE INC.
Inventor： Jianming ZHANG , Zhe LIN
Abstract: Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.
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Patent Agency Ranking
公开(公告)号：US20240404090A1
公开(公告)日：2024-12-05
申请号：US18205413
申请日：2023-06-02
Applicant: ADOBE INC. , Université Laval
Abstract: In various examples, a set of camera parameters associated with an input image are determined based on a disparity map and a signed defocus map. For example, a disparity model generates the disparity map indicating disparity values associated with pixels of the input image and a defocus model generates a signed defocus map indicating blur values associated with the pixels of the input image.
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公开(公告)号：US20240338794A1
公开(公告)日：2024-10-10
申请号：US18352828
申请日：2023-07-14
Applicant: Adobe Inc.
Inventor： Guotong FENG , Jianming ZHANG , Alan ERICKSON
CPC classification number: G06T5/50 , G06T7/194 , G06T2207/20084 , G06T2207/20192 , G06T2207/20221
Abstract: Techniques are disclosed for automatic sky replacement with edge lighting enhancements. A method of automatic sky replacement includes generating a clean mask and a compositing mask for an input image using a mask generation network. A plurality of layers is generated using the clean mask and the compositing mask. The plurality of layers includes an edge lighting layer generated based on a subset of the plurality of layers and the clean mask. A composite image is generated by combining the input image and the plurality of layers including the edge lighting layer.
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公开(公告)号：US20240193802A1
公开(公告)日：2024-06-13
申请号：US18076855
申请日：2022-12-07
Applicant: ADOBE INC.
Inventor： Jianming ZHANG
CPC classification number: G06T7/60 , G06T7/74 , G06T2207/20081
Abstract: A surface normal model is trained to predict normal maps from single images using pair-wise angular losses. A training dataset comprising a training image and a ground truth normal map for the training image is received. To train the surface normal model using the training dataset, a predicted normal map is generated for the training image using the surface normal model. A loss is determined as a function of angular values between pairs of normal vectors for the predicted normal map and corresponding angular values between pairs of normal vectors for the ground truth normal map. The surface normal model is updated based on the loss.
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公开(公告)号：US20220101531A1
公开(公告)日：2022-03-31
申请号：US17479646
申请日：2021-09-20
Applicant: ADOBE INC.
Inventor： Jianming ZHANG , Zhe LIN
Abstract: Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.
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公开(公告)号：US20210342983A1
公开(公告)日：2021-11-04
申请号：US16861548
申请日：2020-04-29
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.
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