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11.发明公开公开(公告)号：US20230360327A1
公开(公告)日：2023-11-09
申请号：US17661878
申请日：2022-05-03
Applicant: Adobe Inc.
Inventor： Sai Bi , Yang Liu , Zexiang Xu , Fujun Luan , Kalyan Sunkavalli
CPC classification number: G06T17/205 , G06T13/20 , G06T2210/21
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：US20230169715A1
公开(公告)日：2023-06-01
申请号：US17538311
申请日：2021-11-30
Applicant: Adobe Inc. , Regents of the University of California
Inventor： Krishna Bhargava Mullia Lakshminarayana , Zexiang Xu , Milos Hasan , Ravi Ramamoorthi , Alexandr Kuznetsov
CPC classification number: G06T15/04 , G06T15/55 , G06N3/0454
Abstract: Methods and systems disclosed herein relate generally to surface-rendering neural networks to represent and render a variety of material appearances (e.g., textured surfaces) at different scales. The system includes receiving image metadata for a texel that includes position, incoming and outgoing radiance direction, and a kernel size. The system applies a offset-prediction neural network to the query to identify an offset coordinate for the texel. The system inputs the offset coordinate to a data structure to determine a feature vector for the texel of the textured surface. The reflectance feature vector is then processed using a decoder neural network to estimate a light-reflectance value of the texel, at which the light-reflectance value is used to render the texel of the textured surface.
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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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14.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：US12211225B2
公开(公告)日：2025-01-28
申请号：US17231833
申请日：2021-04-15
Applicant: ADOBE INC.
Inventor： Sai Bi , Zexiang Xu , Kalyan Krishna Sunkavalli , Miloš Hašan , Yannick Hold-Geoffroy , David Jay Kriegman , Ravi Ramamoorthi
Abstract: A scene reconstruction system renders images of a scene with high-quality geometry and appearance and supports view synthesis, relighting, and scene editing. Given a set of input images of a scene, the scene reconstruction system trains a network to learn a volume representation of the scene that includes separate geometry and reflectance parameters. Using the volume representation, the scene reconstruction system can render images of the scene under arbitrary viewing (view synthesis) and lighting (relighting) locations. Additionally, the scene reconstruction system can render images that change the reflectance of objects in the scene (scene editing).
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Patent Agency Ranking
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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公开(公告)号：US12073507B2
公开(公告)日：2024-08-27
申请号：US17861199
申请日：2022-07-09
Applicant: Adobe Inc.
Inventor： Zexiang Xu , Zhixin Shu , Sai Bi , Qiangeng Xu , Kalyan Sunkavalli , Julien Philip
CPC classification number: G06T15/205 , G06T15/06 , G06T15/80 , G06T2207/10028
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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公开(公告)号：US11816779B2
公开(公告)日：2023-11-14
申请号：US17538311
申请日：2021-11-30
Applicant: Adobe Inc. , The Regents of the University of California
Inventor： Krishna Bhargava Mullia Lakshminarayana , Zexiang Xu , Milos Hasan , Ravi Ramamoorthi , Alexandr Kuznetsov
Abstract: Methods and systems disclosed herein relate generally to surface-rendering neural networks to represent and render a variety of material appearances (e.g., textured surfaces) at different scales. The system includes receiving image metadata for a texel that includes position, incoming and outgoing radiance direction, and a kernel size. The system applies a offset-prediction neural network to the query to identify an offset coordinate for the texel. The system inputs the offset coordinate to a data structure to determine a feature vector for the texel of the textured surface. The reflectance feature vector is then processed using a decoder neural network to estimate a light-reflectance value of the texel, at which the light-reflectance value is used to render the texel of the textured surface.
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19.发明授权公开(公告)号：US11669986B2
公开(公告)日：2023-06-06
申请号：US17233122
申请日：2021-04-16
Applicant: ADOBE INC.
Inventor： Sai Bi , Zexiang Xu , Kalyan Krishna Sunkavalli , David Jay Kriegman , Ravi Ramamoorthi
IPC: G06T7/514 , G06T17/20 , H04N13/111 , H04N13/282 , H04N13/128
CPC classification number: G06T7/514 , G06T17/20 , H04N13/111 , H04N13/128 , H04N13/282 , G06T2207/10012 , G06T2207/10028
Abstract: Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.
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公开(公告)号：US11257284B2
公开(公告)日：2022-02-22
申请号：US15930925
申请日：2020-05-13
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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