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公开(公告)号：US20230079886A1
公开(公告)日：2023-03-16
申请号：US18048311
申请日：2022-10-20
Applicant: Adobe Inc.
Inventor： Sohrab AMIRGHODSI , Zhe LIN , Yilin WANG , Tianshu YU , Connelly BARNES , Elya SHECHTMAN
Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.
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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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公开(公告)号：US20200279355A1
公开(公告)日：2020-09-03
申请号：US16878182
申请日：2020-05-19
Applicant: Adobe Inc.
Inventor： Sohrab AMIRGHODSI , Sarah Jane STUCKEY , Elya SHECHTMAN
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware fill using similarity transformed patches. A user interface receives a user-specified hole and a user-specified sampling region, both of which may be stored in a constraint mask. A brush tool can be used to interactively brush the sampling region and modify the constraint mask. The mask is passed to a patch-based synthesizer configured to synthesize the fill using similarity transformed patches sampled from the sampling region. Fill properties such as similarity transform parameters can be set to control the manner in which the fill is synthesized. A live preview can be provided with gradual updates of the synthesized fill prior to completion. Once a fill has been synthesized, the user interface presents the original image, replacing the hole with the synthesized fill.
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公开(公告)号：US20250054115A1
公开(公告)日：2025-02-13
申请号：US18232212
申请日：2023-08-09
Applicant: Adobe Inc.
Inventor： Zhe LIN , Yuqian ZHOU , Sohrab AMIRGHODSI , Qing LIU , Elya SHECHTMAN , Connelly BARNES , Haitian ZHENG
Abstract: Various disclosed embodiments are directed to resizing, via down-sampling and up-sampling, a high-resolution input image in order to meet machine learning model low-resolution processing requirements, while also producing a high-resolution output image for image inpainting via a machine learning model. Some embodiments use a refinement model to refine the low-resolution inpainting result from the machine learning model such that there will be clear content with high resolution both inside and outside of the mask region in the output. Some embodiments employ new model architecture for the machine learning model that produces the inpainting result—an advanced Cascaded Modulated Generative Adversarial Network (CM-GAN) that includes Fast Fourier Convolution (FCC) layers at the skip connections between the encoder and decoder.
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公开(公告)号：US20240193724A1
公开(公告)日：2024-06-13
申请号：US18076868
申请日：2022-12-07
Applicant: ADOBE INC.
Inventor： Michael Yanis GHARBI , Zhihao XIA , Elya SHECHTMAN , Ke WANG , He ZHANG
CPC classification number: G06T3/4038 , G06T5/005 , G06T7/90 , G06T11/001 , G06T2207/20081 , G06T2207/20212
Abstract: An image processing system employs a parametric model for image harmonization of composite images. The parametric model employs a two-stage approach to harmonize an input composite image. At a first stage, a color curve prediction model predicts color curve parameters for the composite image. At a second stage, the composite image with the color curve parameters are input to a shadow map prediction model, which predicts a shadow map. The predicted color curve parameters and shadow map are applied to the composite image to provide a harmonized composite image. In some aspects, the color curve parameters and shadow map are predicted using a lower-resolution composite image and up-sampled to apply to a higher-resolution version of the composite image. The harmonized composite image can be output with the predicted color curve parameters and/or shadow map, which can be modified by a user to further enhance the harmonized composite image.
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Patent Agency Ranking
公开(公告)号：US20240070884A1
公开(公告)日：2024-02-29
申请号：US17896574
申请日：2022-08-26
Applicant: ADOBE INC.
Inventor： Taesung PARK , Sylvain PARIS , Richard ZHANG , Elya SHECHTMAN
CPC classification number: G06T7/40 , G06T5/50 , G06T2207/10028
Abstract: An image processing system uses a depth-conditioned autoencoder to generate a modified image from an input image such that the modified image maintains an overall structure from the input image while modifying textural features. An encoder of the depth-conditioned autoencoder extracts a structure latent code from an input image and depth information for the input image. A generator of the depth-conditioned autoencoder generates a modified image using the structure latent code and a texture latent code. The modified image generated by the depth-conditioned autoencoder includes the structural features from the input image while incorporating textural features of the texture latent code. In some aspects, the autoencoder is depth-conditioned during training by augmenting training images with depth information. The autoencoder is trained to preserve the depth information when generating images.
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公开(公告)号：US20240028871A1
公开(公告)日：2024-01-25
申请号：US17870496
申请日：2022-07-21
Applicant: Adobe Inc.
Inventor： Mang Tik CHIU , Connelly BARNES , Zijun WEI , Zhe LIN , Yuqian ZHOU , Xuaner ZHANG , Sohrab AMIRGHODSI , Florian KAINZ , Elya SHECHTMAN
CPC classification number: G06N3/0454 , G06T5/005 , G06T5/30 , G06T7/62 , G06T3/40
Abstract: Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.
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公开(公告)号：US20230086807A1
公开(公告)日：2023-03-23
申请号：US17723879
申请日：2022-04-19
Applicant: Adobe Inc. , Czech Technical University in Prague
Inventor： Michal LUKÁC , Elya SHECHTMAN , Daniel SÝKORA , David FUTSCHIK
Abstract: Embodiments are disclosed for segmented image generation. The method may include receiving an input image and a segmentation mask, projecting, using a differentiable machine learning pipeline, a plurality of segments of the input image into a plurality of latent spaces associated with a plurality of generators to obtain a plurality of projected segments, and compositing the plurality of projected segments into an output image.
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公开(公告)号：US20220156522A1
公开(公告)日：2022-05-19
申请号：US16951782
申请日：2020-11-18
Applicant: Adobe Inc.
Inventor： Elya SHECHTMAN , William PEEBLES , Richard ZHANG , Jun-Yan ZHU , Alyosha EFROS
Abstract: Embodiments are disclosed for generative image congealing which provides an unsupervised learning technique that learns transformations of real data to improve the image quality of GANs trained using that image data. In particular, in one or more embodiments, the disclosed systems and methods comprise generating, by a spatial transformer network, an aligned real image for a real image from an unaligned real dataset, providing, by the spatial transformer network, the aligned real image to an adversarial discrimination network to determine if the aligned real image resembles aligned synthetic images generated by a generator network, and training, by a training manager, the spatial transformer network to learn updated transformations based on the determination of the adversarial discrimination network.
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公开(公告)号：US20230070666A1
公开(公告)日：2023-03-09
申请号：US17466711
申请日：2021-09-03
Applicant: Adobe Inc. , Czech Technical University in Prague
Inventor： Michal LUKÁC , Daniel SÝKORA , David FUTSCHIK , Zhaowen WANG , Elya SHECHTMAN
Abstract: Embodiments are disclosed for translating an image from a source visual domain to a target visual domain. In particular, in one or more embodiments, the disclosed systems and methods comprise a training process that includes receiving a training input including a pair of keyframes and an unpaired image. The pair of keyframes represent a visual translation from a first version of an image in a source visual domain to a second version of the image in a target visual domain. The one or more embodiments further include sending the pair of keyframes and the unpaired image to an image translation network to generate a first training image and a second training image. The one or more embodiments further include training the image translation network to translate images from the source visual domain to the target visual domain based on a calculated loss using the first and second training images.
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