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In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image.
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Cgintrinsics: Better intrinsic image decomposition through physically-based rendering
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Duan Gao, Xiao Li, Yue Dong, Pieter Peers, Kun Xu, and Xin Tong · 2019
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Fast spatially-varying indoor lighting estimation
Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, and Jean-Francois Lalonde · 2019
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An approximate shading model with detail decomposition for object relighting
Zicheng Liao, Kevin Karsch, Hongyi Zhang, and David Forsyth · 2019
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Neural inverse rendering of an indoor scene from a single image
Soumyadip Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu, David W Jacobs, and Jan Kautz · 2019
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Glosh: Global-local spherical harmonics for intrinsic image decomposition
Hao Zhou, Xiang Yu, and David W Jacobs · 2019
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Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang · 2019
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Louis-Philippe Asselin, Denis Laurendeau, and Jean-François Lalonde · 2020
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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Photorealistic text-to-image diffusion models with deep language understanding
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Photoscene: Photorealistic material and lighting transfer for indoor scenes
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