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公开(公告)号：US11164343B1
公开(公告)日：2021-11-02
申请号：US17067675
申请日：2020-10-10
Applicant: Adobe Inc.
Inventor： Vineet Batra , Praveen Kumar Dhanuka , Nathan Carr , Ankit Phogat
IPC: G06T11/00 , G06T11/40 , G06F3/08 , G06F3/0488
Abstract: Techniques are disclosed for populating a region of an image with a plurality of brush strokes. For instance, the image is displayed, with the region of the image bounded by a boundary. A user input is received that is indicative of a user-defined brush stroke within the region. One or more synthesized brush strokes are generated within the region, based on the user-defined brush stroke. In some examples, the one or more synthesized brush strokes fill at least a part of the region of the image. The image is displayed, along with the user-defined brush stroke and the one or more synthesized brush strokes within the region of the image.
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公开(公告)号：US20210319256A1
公开(公告)日：2021-10-14
申请号：US17332773
申请日：2021-05-27
Applicant: Adobe Inc.
Inventor： Xin Sun , Sohrab Amirghodsi , Nathan Carr , Michal Lukac
Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.
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公开(公告)号：US10665011B1
公开(公告)日：2020-05-26
申请号：US16428482
申请日：2019-05-31
Applicant: Adobe Inc. , Université Laval
Inventor： Kalyan Sunkavalli , Sunil Hadap , Nathan Carr , Jean-Francois Lalonde , Mathieu Garon
Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that use a local-lighting-estimation-neural network to render a virtual object in a digital scene by using a local-lighting-estimation-neural network to analyze both global and local features of the digital scene and generate location-specific-lighting parameters for a designated position within the digital scene. For example, the disclosed systems extract and combine such global and local features from a digital scene using global network layers and local network layers of the local-lighting-estimation-neural network. In certain implementations, the disclosed systems can generate location-specific-lighting parameters using a neural-network architecture that combines global and local feature vectors to spatially vary lighting for different positions within a digital scene.
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公开(公告)号：US20200082610A1
公开(公告)日：2020-03-12
申请号：US16126552
申请日：2018-09-10
Applicant: Adobe Inc.
Inventor： Xin Sun , Zhili Chen , Nathan Carr , Julio Marco Murria , Jimei Yang
Abstract: According to one general aspect, systems and techniques for rendering a painting stroke of a three-dimensional digital painting include receiving a painting stroke input on a canvas, where the painting stroke includes a plurality of pixels. For each of the pixels in the plurality of pixels, a neighborhood patch of pixels is selected and input into a neural network and a shading function is output from the neural network. The painting stroke is rendered on the canvas using the shading function.
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公开(公告)号：US12277652B2
公开(公告)日：2025-04-15
申请号：US18055585
申请日：2022-11-15
Applicant: Adobe Inc.
Inventor： Radomir Mech , Nathan Carr , Matheus Gadelha
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.
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Patent Agency Ranking
26.发明公开公开(公告)号：US20240161406A1
公开(公告)日：2024-05-16
申请号：US18055590
申请日：2022-11-15
Applicant: Adobe Inc.
Inventor： Radomir Mech , Nathan Carr , Matheus Gadelha
CPC classification number: G06T17/205 , G06T5/005 , G06T2207/20084
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.
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27.发明公开公开(公告)号：US20240161405A1
公开(公告)日：2024-05-16
申请号：US18055585
申请日：2022-11-15
Applicant: Adobe Inc.
Inventor： Radomir Mech , Nathan Carr , Matheus Gadelha
CPC classification number: G06T17/205 , G06T7/11 , G06T7/50 , G06T7/70 , G06T11/60 , G06V20/70 , G06T2200/08 , G06T2200/24 , G06T2207/20021 , G06T2207/20084 , G06T2207/20228
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.
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28.发明公开公开(公告)号：US20240135612A1
公开(公告)日：2024-04-25
申请号：US18304113
申请日：2023-04-20
Applicant: Adobe Inc.
Inventor： Yannick Hold-Geoffroy , Vojtech Krs , Radomir Mech , Nathan Carr , Matheus Gadelha
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.
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公开(公告)号：US11880766B2
公开(公告)日：2024-01-23
申请号：US17384357
申请日：2021-07-23
Applicant: Adobe Inc.
Inventor： Cameron Smith , Ratheesh Kalarot , Wei-An Lin , Richard Zhang , Niloy Mitra , Elya Shechtman , Shabnam Ghadar , Zhixin Shu , Yannick Hold-Geoffrey , Nathan Carr , Jingwan Lu , Oliver Wang , Jun-Yan Zhu
IPC: G06N3/08 , G06F3/04845 , G06F3/04847 , G06T11/60 , G06T3/40 , G06N20/20 , G06T5/00 , G06T5/20 , G06T3/00 , G06T11/00 , G06F18/40 , G06F18/211 , G06F18/214 , G06F18/21 , G06N3/045
CPC classification number: G06N3/08 , G06F3/04845 , G06F3/04847 , G06F18/211 , G06F18/214 , G06F18/2163 , G06F18/40 , G06N3/045 , G06N20/20 , G06T3/0006 , G06T3/0093 , G06T3/40 , G06T3/4038 , G06T3/4046 , G06T5/005 , G06T5/20 , G06T11/001 , G06T11/60 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2210/22
Abstract: An improved system architecture uses a pipeline including a Generative Adversarial Network (GAN) including a generator neural network and a discriminator neural network to generate an image. An input image in a first domain and information about a target domain are obtained. The domains correspond to image styles. An initial latent space representation of the input image is produced by encoding the input image. An initial output image is generated by processing the initial latent space representation with the generator neural network. Using the discriminator neural network, a score is computed indicating whether the initial output image is in the target domain. A loss is computed based on the computed score. The loss is minimized to compute an updated latent space representation. The updated latent space representation is processed with the generator neural network to generate an output image in the target domain.
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公开(公告)号：US11823313B2
公开(公告)日：2023-11-21
申请号：US17332773
申请日：2021-05-27
Applicant: Adobe Inc.
Inventor： Xin Sun , Sohrab Amirghodsi , Nathan Carr , Michal Lukac
CPC classification number: G06T11/60 , G06V10/758
Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.
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