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arXiv · 2605.21766

BodyReLux: Temporally Consistent Full-Body Video Relighting

Li Ma·Mingming He·Xueming Yu·David M. George·Ahmet Levent Taşel·Paul Debevec·Julien Philip
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Abstract

Being able to relight human performance is a fundamental task for post production and content creation. We present BodyReLux, a subject-specific video diffusion-based framework for relighting full-body human performances in a temporally consistent way. Our model is trained on a hybrid dataset of pixel-aligned video relighting pairs, covering a diverse combination of lighting conditions, performances and viewpoints. To acquire such dataset, we combine traditional static One-Light-at-a-Time (OLAT) capture and a novel dynamic performance capture in which two smoothly varying lighting sequences are rapidly interleaved. Because the lighting operates above the human flicker-fusion threshold, the interleaving does not appear to strobe. We train our video relighting model from a pretrained text-to-video model to fully leverage the generative priors for producing high quality videos. To achieve accurate lighting control, we introduce a new lighting conditioning method that represents each light source as a token. We further condition on sequences of lighting using masked attention to support dynamic lighting control. Together with a carefully designed data augmentation pipeline, we achieve photorealistic, robust, and temporally consistent video relighting of subject-specific human performances.

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Li Ma, Mingming He, Xueming Yu, David M. George, Ahmet Levent Taşel, Paul Debevec, Julien Philip. 2026-05-20. BodyReLux: Temporally Consistent Full-Body Video Relighting. https://doi.org/10.1145/3811352

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