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公开(公告)号：US20220335671A1
公开(公告)日：2022-10-20
申请号：US17232890
申请日：2021-04-16
Applicant: ADOBE INC
Inventor： Alan Erickson , Kalyan Sunkavalli , I-Ming Pao , Guotong Feng , Jianming Zhang , Frederick Mandia
Abstract: Systems and methods for image editing are described. Embodiments of the present disclosure provide an image editing system for performing image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. According to some embodiments described herein, real-time color harmonization based on the visible sky region may be used to produce more natural colorization. In some examples, horizon-aware sky alignment and placement with advanced padding may also be used. For example, the horizons of the original image and the replacement image may be automatically detected and aligned, and color harmonization may be performed based on the aligned images.
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公开(公告)号：US20220292654A1
公开(公告)日：2022-09-15
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
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：US20210343051A1
公开(公告)日：2021-11-04
申请号：US16863540
申请日：2020-04-30
Applicant: Adobe Inc.
Inventor： Milos Hasan , Liang Shi , Tamy Boubekeur , Kalyan Sunkavalli , Radomir Mech
Abstract: The present disclosure relates to using end-to-end differentiable pipeline for optimizing parameters of a base procedural material to generate a procedural material corresponding to a target physical material. For example, the disclosed systems can receive a digital image of a target physical material. In response, the disclosed systems can retrieve a differentiable procedural material for use as a base procedural material in response. The disclosed systems can compare a digital image of the base procedural material with the digital image of the target physical material using a loss function, such as a style loss function that compares visual appearance. Based on the determined loss, the disclosed systems can modify the parameters of the base procedural material to determine procedural material parameters for the target physical material. The disclosed systems can generate a procedural material corresponding to the base procedural material using the determined procedural material parameters.
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公开(公告)号：US11158090B2
公开(公告)日：2021-10-26
申请号：US16692503
申请日：2019-11-22
Applicant: Adobe Inc.
Inventor： Tharun Mohandoss , Pulkit Gera , Oliver Wang , Kartik Sethi , Kalyan Sunkavalli , Elya Shechtman , Chetan Nanda
Abstract: This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.
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公开(公告)号：US20210150333A1
公开(公告)日：2021-05-20
申请号：US16686978
申请日：2019-11-18
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhihao Xia , Michael Gharbi , Kalyan Sunkavalli
Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.
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Patent Agency Ranking
公开(公告)号：US20200302684A1
公开(公告)日：2020-09-24
申请号：US16877227
申请日：2020-05-18
Applicant: ADOBE INC.
Inventor： Kalyan Sunkavalli , Sunil Hadap , Nathan Carr , Mathieu Garon
Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that use a local-lighting-estimation-neural network to estimate lighting parameters for specific positions within a digital scene for augmented reality. For example, based on a request to render a virtual object in a digital scene, a system uses a local-lighting-estimation-neural network to generate location-specific-lighting parameters for a designated position within the digital scene. In certain implementations, the system also renders a modified digital scene comprising the virtual object at the designated position according to the parameters. In some embodiments, the system generates such location-specific-lighting parameters to spatially vary and adapt lighting conditions for different positions within a digital scene. As requests to render a virtual object come in real (or near real) time, the system can quickly generate different location-specific-lighting parameters that accurately reflect lighting conditions at different positions within a digital scene in response to render requests.
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公开(公告)号：US10692276B2
公开(公告)日：2020-06-23
申请号：US15970367
申请日：2018-05-03
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Zexiang Xu , Sunil Hadap
Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.
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公开(公告)号：US10475169B2
公开(公告)日：2019-11-12
申请号：US15824943
申请日：2017-11-28
Applicant: Adobe Inc.
Inventor： Kalyan Sunkavalli , Mehmet Ersin Yumer , Marc-Andre Gardner , Xiaohui Shen , Jonathan Eisenmann , Emiliano Gambaretto
Abstract: Systems and techniques for estimating illumination from a single image are provided. An example system may include a neural network. The neural network may include an encoder that is configured to encode an input image into an intermediate representation. The neural network may also include an intensity decoder that is configured to decode the intermediate representation into an output light intensity map. An example intensity decoder is generated by a multi-phase training process that includes a first phase to train a light mask decoder using a set of low dynamic range images and a second phase to adjust parameters of the light mask decoder using a set of high dynamic range image to generate the intensity decoder.
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39.发明授权公开(公告)号：US12254570B2
公开(公告)日：2025-03-18
申请号：US17661878
申请日：2022-05-03
Applicant: Adobe Inc.
Inventor： Sai Bi , Yang Liu , Zexiang Xu , Fujun Luan , Kalyan Sunkavalli
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate three-dimensional hybrid mesh-volumetric representations for digital objects. For instance, in one or more embodiments, the disclosed systems generate a mesh for a digital object from a plurality of digital images that portray the digital object using a multi-view stereo model. Additionally, the disclosed systems determine a set of sample points for a thin volume around the mesh. Using a neural network, the disclosed systems further generate a three-dimensional hybrid mesh-volumetric representation for the digital object utilizing the set of sample points for the thin volume and the mesh.
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公开(公告)号：US20240404181A1
公开(公告)日：2024-12-05
申请号：US18799247
申请日：2024-08-09
Applicant: Adobe Inc.
Inventor： Zexiang Xu , Zhixin Shu , Sai Bi , Qiangeng Xu , Kalyan Sunkavalli , Julien Philip
Abstract: A scene modeling system receives a plurality of input two-dimensional (2D) images corresponding to a plurality of views of an object and a request to display a three-dimensional (3D) scene that includes the object. The scene modeling system generates an output 2D image for a view of the 3D scene by applying a scene representation model to the input 2D images. The scene representation model includes a point cloud generation model configured to generate, based on the input 2D images, a neural point cloud representing the 3D scene. The scene representation model includes a neural point volume rendering model configured to determine, for each pixel of the output image and using the neural point cloud and a volume rendering process, a color value. The scene modeling system transmits, responsive to the request, the output 2D image. Each pixel of the output image includes the respective determined color value.
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