Light-A-Video: Training-free Video Relighting via Progressive Light
  Fusion
Authors: name: Yujie Zhou, name: Jiazi Bu, name: Pengyang Ling, name: Pan Zhang, name: Tong Wu, name: Qidong Huang, name: Jinsong Li, name: Xiaoyi Dong, name: Yuhang Zang, name: Yuhang Cao, name: Anyi Rao, name: Jiaqi Wang, name: Li Niu

Recent advancements in image relighting models, driven by large-scale
datasets and pre-trained diffusion models, have enabled the imposition of
consistent lighting. However, video relighting still lags, primarily due to the
excessive training costs and the scarcity of diverse, high-quality video
relighting datasets. A simple application of image relighting models on a
frame-by-frame basis leads to several issues: lighting source inconsistency and
relighted appearance inconsistency, resulting in flickers in the generated
videos. In this work, we propose Light-A-Video, a training-free approach to
achieve temporally smooth video relighting. Adapted from image relighting
models, Light-A-Video introduces two key techniques to enhance lighting
consistency. First, we design a Consistent Light Attention (CLA) module, which
enhances cross-frame interactions within the self-attention layers to stabilize
the generation of the background lighting source. Second, leveraging the
physical principle of light transport independence, we apply linear blending
between the source video's appearance and the relighted appearance, using a
Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions
in illumination. Experiments show that Light-A-Video improves the temporal
consistency of relighted video while maintaining the image quality, ensuring
coherent lighting transitions across frames. Project page:
https://bujiazi.github.io/light-a-video.github.io/.