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BodyReLux framework enables consistent full-body video relighting

Researchers have developed BodyReLux, a novel framework that uses a diffusion-based approach to relight full-body human performances in videos with temporal consistency. The system is trained on a unique dataset combining traditional static capture with a dynamic performance capture method. By leveraging pretrained text-to-video models and a new token-based lighting conditioning technique, BodyReLux achieves photorealistic and robust video relighting, enabling dynamic control over lighting sequences. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a new method for video relighting, potentially impacting content creation and post-production workflows.

RANK_REASON The cluster contains an academic paper detailing a new technical framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Li Ma, Mingming He, Xueming Yu, David M. George, Ahmet Levent Ta\c{s}el, Paul Debevec, Julien Philip ·

    BodyReLux: Temporally Consistent Full-Body Video Relighting

    arXiv:2605.21766v1 Announce Type: new 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 tempor…