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41.
公开(公告)号:US11538216B2
公开(公告)日:2022-12-27
申请号:US16558975
申请日:2019-09-03
Applicant: Adobe Inc. , Université Laval
Inventor: Kalyan Sunkavalli , Yannick Hold-Geoffroy , Christian Gagne , Marc-Andre Gardner , Jean-Francois Lalonde
Abstract: 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.
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公开(公告)号:US20210073955A1
公开(公告)日:2021-03-11
申请号:US16564398
申请日:2019-09-09
Applicant: ADOBE INC.
Inventor: Jinsong Zhang , Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Jonathan Eisenmann , Jean-Francois Lalonde
IPC: G06T5/00
Abstract: Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate high-dynamic range lighting parameters for input low-dynamic range images. The high-dynamic range lighting parameters can be based on sky color, sky turbidity, sun color, sun shape, and sun position. Such input low-dynamic range images can be low-dynamic range panorama images or low-dynamic range standard images. Such a neural network system can apply the estimates high-dynamic range lighting parameters to objects added to the low-dynamic range images.
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