公开(公告)号：US20230098115A1

公开(公告)日：2023-03-30

申请号：US18062460

申请日：2022-12-06

申请人： Adobe Inc. ,  Université Laval

发明人： Kalyan Sunkavalli ,  Yannick Hold-Geoffroy ,  Christian Gagne ,  Marc-Andre Gardner ,  Jean-Francois Lalonde

IPC分类号： G06T15/50 ,  G06T7/90 ,  G06T7/60 ,  G06T7/70 ,  G06N3/08 ,  G06T7/50

摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.

公开(公告)号：US20210065440A1

公开(公告)日：2021-03-04

申请号：US16558975

申请日：2019-09-03

申请人： Adobe Inc. ,  Université Laval

发明人： Kalyan Sunkavalli ,  Yannick Hold-Geoffroy ,  Christian Gagne ,  Marc-Andre Gardner ,  Jean-Francois Lalonde

IPC分类号： G06T15/50 ,  G06T7/50 ,  G06T7/90 ,  G06T7/70 ,  G06T7/60 ,  G06N3/08

摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.

公开(公告)号：US12008710B2

公开(公告)日：2024-06-11

申请号：US18062460

申请日：2022-12-06

申请人： Adobe Inc. ,  Université Laval

发明人： Kalyan Sunkavalli ,  Yannick Hold-Geoffroy ,  Christian Gagne ,  Marc-Andre Gardner ,  Jean-Francois Lalonde

IPC分类号： G06T15/00 ,  G06N3/08 ,  G06T7/50 ,  G06T7/60 ,  G06T7/70 ,  G06T7/90 ,  G06T15/50

CPC分类号： G06T15/506 ,  G06N3/08 ,  G06T7/50 ,  G06T7/60 ,  G06T7/70 ,  G06T7/90 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084

摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.

公开(公告)号：US11538216B2

公开(公告)日：2022-12-27

申请号：US16558975

申请日：2019-09-03

申请人： Adobe Inc. ,  Université Laval

发明人： Kalyan Sunkavalli ,  Yannick Hold-Geoffroy ,  Christian Gagne ,  Marc-Andre Gardner ,  Jean-Francois Lalonde

IPC分类号： G06T15/00 ,  G06T15/50 ,  G06T7/50 ,  G06T7/90 ,  G06T7/70 ,  G06N3/08 ,  G06T7/60

摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.