Light-A-Video: Training-free Video Relighting via Progressive Light
  Fusion
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/.
67ad79552fdac6537b43f120
Yujie Zhou
claimed_verified
2025-03-16T21:19:33.219Z
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yujie
user
yujieouo
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Jiazi Bu
67ad79552fdac6537b43f122
Pengyang Ling
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Pan Zhang
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Tong Wu
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Qidong Huang
67ad79552fdac6537b43f126
Jinsong Li
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Xiaoyi Dong
67ad79552fdac6537b43f128
Yuhang Zang
claimed_verified
2025-02-13T08:21:31.817Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/mCgynEtoXdILvzyoPexii.png
Yuhang Zang
user
yuhangzang
67ad79552fdac6537b43f129
Yuhang Cao
67ad79552fdac6537b43f12a
Anyi Rao
67ad79552fdac6537b43f12b
Jiaqi Wang
claimed_verified
2025-05-21T09:07:30.165Z
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/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
Jiaqi Wang
user
myownskyW7
67ad79552fdac6537b43f12c
Li Niu
Light-A-Video: Training-free Video Relighting via Progressive Light
  Fusion
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/.
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.
Consistent Light Attention CLA Progressive Light Fusion PLF image relighting models self-attention layers light transport independence linear blending