Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes from source videos can be similarly leveraged to enhance both fidelity and temporal coherence in video face swapping. Building on this insight, this work presents LivingSwap, the first video reference guided face swapping model. Our approach employs keyframes as conditioning signals to inject the target identity, enabling flexible and controllable editing. By combining keyframe conditioning with video reference guidance, the model performs temporal stitching to ensure stable identity preservation and high-fidelity reconstruction across long video sequences. To address the scarcity of data for reference-guided training, we construct a paired face-swapping dataset, Face2Face, and further reverse the data pairs to ensure reliable ground-truth supervision. Extensive experiments demonstrate that our method achieves state-of-the-art results, seamlessly integrating the target identity with the source video's expressions, lighting, and motion, while significantly reducing manual effort in production workflows. Project webpage: https://aim-uofa.github.io/LivingSwap
6938e892dfc35938ba129ff5
Zekai Luo
6938e892dfc35938ba129ff6
Zongze Du
6938e892dfc35938ba129ff7
Zhouhang Zhu
6938e892dfc35938ba129ff8
Hao Zhong
6938e892dfc35938ba129ff9
Muzhi Zhu
admin_assigned
2025-12-10T10:51:43.541Z
632179745fc60c44fd91fc33
/avatars/37d4fefbcc19f091dccffefec9706de2.svg
zhumuzhi
Z-MU-Z
user
Z-MU-Z
6938e892dfc35938ba129ffa
Wen Wang
claimed_verified
2025-12-12T09:17:46.292Z
63f089456309c84d5f47f951
/avatars/04b926a7f2ad091ee00fef0c59903492.svg
Wen Wang
wwen1997
user
wwen1997
6938e892dfc35938ba129ffb
Yuling Xi
6938e892dfc35938ba129ffc
Chenchen Jing
6938e892dfc35938ba129ffd
Hao Chen
6938e892dfc35938ba129ffe
Chunhua Shen
Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes from source videos can be similarly leveraged to enhance both fidelity and temporal coherence in video face swapping. Building on this insight, this work presents LivingSwap, the first video reference guided face swapping model. Our approach employs keyframes as conditioning signals to inject the target identity, enabling flexible and controllable editing. By combining keyframe conditioning with video reference guidance, the model performs temporal stitching to ensure stable identity preservation and high-fidelity reconstruction across long video sequences. To address the scarcity of data for reference-guided training, we construct a paired face-swapping dataset, Face2Face, and further reverse the data pairs to ensure reliable ground-truth supervision. Extensive experiments demonstrate that our method achieves state-of-the-art results, seamlessly integrating the target identity with the source video's expressions, lighting, and motion, while significantly reducing manual effort in production workflows. Project webpage: https://aim-uofa.github.io/LivingSwap
LivingSwap enhances video face swapping by using keyframes and reference guidance to maintain identity and fidelity over long sequences, reducing manual effort and achieving state-of-the-art results.
keyframe conditioning video reference guidance temporal stitching identity preservation high-fidelity reconstruction Face2Face dataset