TC-Light: Temporally Consistent Relighting for Dynamic Long Videos
Editing illumination in long videos with complex dynamics has significant
value in various downstream tasks, including visual content creation and
manipulation, as well as data scaling up for embodied AI through sim2real and
real2real transfer. Nevertheless, existing video relighting techniques are
predominantly limited to portrait videos or fall into the bottleneck of
temporal consistency and computation efficiency. In this paper, we propose
TC-Light, a novel paradigm characterized by the proposed two-stage post
optimization mechanism. Starting from the video preliminarily relighted by an
inflated video relighting model, it optimizes appearance embedding in the first
stage to align global illumination. Then it optimizes the proposed canonical
video representation, i.e., Unique Video Tensor (UVT), to align fine-grained
texture and lighting in the second stage. To comprehensively evaluate
performance, we also establish a long and highly dynamic video benchmark.
Extensive experiments show that our method enables physically plausible
relighting results with superior temporal coherence and low computation cost.
The code and video demos are available at
https://dekuliutesla.github.io/tclight/.
685a9b770e4ad7e219758743
Yang Liu
claimed_verified
2025-06-24T13:33:35.287Z
66ef6fd0ea7d19a2399d6b1f
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rJIMhaB4NB8bvBtmsw1vA.png
Yang Liu
user
TeslaYang123
685a9b770e4ad7e219758744
Chuanchen Luo
admin_assigned
2025-06-24T13:18:30.531Z
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Chuanchen Luo
user
LogicTrainer
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Zimo Tang
685a9b770e4ad7e219758746
Yingyan Li
685a9b770e4ad7e219758747
Yuran Yang
685a9b770e4ad7e219758748
Yuanyong Ning
685a9b770e4ad7e219758749
Lue Fan
685a9b770e4ad7e21975874a
Junran Peng
admin_assigned
2025-06-24T13:18:38.122Z
62c11a7360edb2dd7765b80d
/avatars/92db3258d0ba44daee22952f0644cd93.svg
Junran Peng
user
JrPeng
685a9b770e4ad7e21975874b
Zhaoxiang Zhang
TC-Light: Temporally Consistent Relighting for Dynamic Long Videos
Editing illumination in long videos with complex dynamics has significant
value in various downstream tasks, including visual content creation and
manipulation, as well as data scaling up for embodied AI through sim2real and
real2real transfer. Nevertheless, existing video relighting techniques are
predominantly limited to portrait videos or fall into the bottleneck of
temporal consistency and computation efficiency. In this paper, we propose
TC-Light, a novel paradigm characterized by the proposed two-stage post
optimization mechanism. Starting from the video preliminarily relighted by an
inflated video relighting model, it optimizes appearance embedding in the first
stage to align global illumination. Then it optimizes the proposed canonical
video representation, i.e., Unique Video Tensor (UVT), to align fine-grained
texture and lighting in the second stage. To comprehensively evaluate
performance, we also establish a long and highly dynamic video benchmark.
Extensive experiments show that our method enables physically plausible
relighting results with superior temporal coherence and low computation cost.
The code and video demos are available at
https://dekuliutesla.github.io/tclight/.
TC-Light, a novel two-stage video relighting model, achieves high temporal coherence and low computational cost through appearance embedding and unique video tensor optimization.
video relighting inflated video relighting model appearance embedding canonical video representation Unique Video Tensor UVT physically plausible relighting temporal coherence computational cost