Lumen: Consistent Video Relighting and Harmonious Background Replacement
  with Video Generative Models
Authors: name: Jianshu Zeng; name: Yuxuan Liu; name: Yutong Feng; name: Chenxuan Miao; name: Zixiang Gao; name: Jiwang Qu; name: Jianzhang Zhang; name: Bin Wang; name: Kun Yuan
Published: 2025-08-18 14:21:22

Abstract: Video relighting is a challenging yet valuable task, aiming to replace the
background in videos while correspondingly adjusting the lighting in the
foreground with harmonious blending. During translation, it is essential to
preserve the original properties of the foreground, e.g., albedo, and propagate
consistent relighting among temporal frames. In this paper, we propose Lumen,
an end-to-end video relighting framework developed on large-scale video
generative models, receiving flexible textual description for instructing the
control of lighting and background. Considering the scarcity of high-qualified
paired videos with the same foreground in various lighting conditions, we
construct a large-scale dataset with a mixture of realistic and synthetic
videos. For the synthetic domain, benefiting from the abundant 3D assets in the
community, we leverage advanced 3D rendering engine to curate video pairs in
diverse environments. For the realistic domain, we adapt a HDR-based lighting
simulation to complement the lack of paired in-the-wild videos. Powered by the
aforementioned dataset, we design a joint training curriculum to effectively
unleash the strengths of each domain, i.e., the physical consistency in
synthetic videos, and the generalized domain distribution in realistic videos.
To implement this, we inject a domain-aware adapter into the model to decouple
the learning of relighting and domain appearance distribution. We construct a
comprehensive benchmark to evaluate Lumen together with existing methods, from
the perspectives of foreground preservation and video consistency assessment.
Experimental results demonstrate that Lumen effectively edit the input into
cinematic relighted videos with consistent lighting and strict foreground
preservation. Our project page: https://lumen-relight.github.io/