title: Light-A-Video: Training-free Video Relighting via Progressive Light
    Fusion
summary: 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/.
paper_authors._id: 67ad79552fdac6537b43f120
paper_authors.name: Yujie Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:19:33.219Z
paper_authors._id: 64b51fd8bcfd8542d6473d9a
paper_authors.avatarUrl: /avatars/ceaa73b79f448996187f07733d96b800.svg
paper_authors.fullname: yujie
paper_authors.type: user
paper_authors.user: yujieouo
paper_authors._id: 67ad79552fdac6537b43f121
paper_authors.name: Jiazi Bu
paper_authors._id: 67ad79552fdac6537b43f122
paper_authors.name: Pengyang Ling
paper_authors._id: 67ad79552fdac6537b43f123
paper_authors.name: Pan Zhang
paper_authors._id: 67ad79552fdac6537b43f124
paper_authors.name: Tong Wu
paper_authors._id: 67ad79552fdac6537b43f125
paper_authors.name: Qidong Huang
paper_authors._id: 67ad79552fdac6537b43f126
paper_authors.name: Jinsong Li
paper_authors._id: 67ad79552fdac6537b43f127
paper_authors.name: Xiaoyi Dong
paper_authors._id: 67ad79552fdac6537b43f128
paper_authors.name: Yuhang Zang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:21:31.817Z
paper_authors._id: 63859cf3b2906edaf83af9f0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/mCgynEtoXdILvzyoPexii.png
paper_authors.fullname: Yuhang Zang
paper_authors.type: user
paper_authors.user: yuhangzang
paper_authors._id: 67ad79552fdac6537b43f129
paper_authors.name: Yuhang Cao
paper_authors._id: 67ad79552fdac6537b43f12a
paper_authors.name: Anyi Rao
paper_authors._id: 67ad79552fdac6537b43f12b
paper_authors.name: Jiaqi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:07:30.165Z
paper_authors._id: 64b4eec4faa3181a5eab9c46
paper_authors.avatarUrl: /avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
paper_authors.fullname: Jiaqi Wang
paper_authors.type: user
paper_authors.user: myownskyW7
paper_authors._id: 67ad79552fdac6537b43f12c
paper_authors.name: Li Niu
paper_title: Light-A-Video: Training-free Video Relighting via Progressive Light
    Fusion
paper_summary: 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/.
paper_ai_summary: Light-A-Video achieves temporally smooth video relighting through a training-free approach, using a Consistent Light Attention module and Progressive Light Fusion strategy to enhance lighting consistency and maintain image quality.
paper_ai_keywords: Consistent Light Attention CLA Progressive Light Fusion PLF image relighting models self-attention layers light transport independence linear blending