All‐frequency Full‐body Human Image Relighting

Abstract Relighting of human images enables post‐photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high‐frequency shadows and shading. In this paper, we propose a two‐stage relighting method that can reproduce physically‐based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically‐based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area‐light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all‐frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.

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Publication Details

Journal
Computer Graphics Forum
Published
2025-04-07
DOI
https://doi.org/10.1111/cgf.70007
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

All‐frequency Full‐body Human Image Relighting

Yoshihiro Kanamori, Daichi Tajima, Yuki Endo
Computer Graphics Forum
Advanced Vision and Imaging
article

All‐frequency Full‐body Human Image Relighting

Yoshihiro Kanamori, Daichi Tajima, Yuki Endo
article en

Abstract

Abstract Relighting of human images enables post‐photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high‐frequency shadows and shading. In this paper, we propose a two‐stage relighting method that can reproduce physically‐based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically‐based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area‐light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all‐frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.

Computer Graphics ForumVol. 44(2)
University of Tsukuba (JP)
Japan Science and Technology Corporation, Japan Society for the Promotion of Science
Openalex Percentile: Top 94%
Advanced Vision and Imaging
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