保留源视频真实感：面向电影级画质的高保真换脸
Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
把参考引导图像编辑中的「参考图注入属性」思路迁移到视频换脸：用关键帧作为身份条件信号、用源视频作为参考引导，并通过时序拼接保证长视频的身份稳定与高保真重建，为长视频可控编辑提供了可复用的条件注入与时序缝合范式。
原文依据 Our approach employs keyframes as conditioning signals to inject the target identity, enabling flexible and controllable editing.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视频换脸在电影与娱乐制作中至关重要，而在长且复杂的视频序列上实现高保真与时序一致性仍是一项重大挑战。受近期参考引导图像编辑进展的启发，我们探索能否类似地利用源视频中丰富的视觉属性来同时提升视频换脸的保真度与时序连贯性。基于这一思路，本文提出 LivingSwap，这是首个视频参考引导的换脸模型。我们的方法采用关键帧作为条件信号来注入目标身份，从而实现灵活且可控的编辑。通过将关键帧条件与视频参考引导相结合，模型执行时序拼接，以在长视频序列上确保稳定的身份保持与高保真重建。为缓解参考引导训练数据稀缺的问题，我们构建了配对换脸数据集 Face2Face，并进一步反转数据对以确保可靠的真值监督。大量实验表明，我们的方法取得了最优结果，将目标身份与源视频的表情、光照和运动无缝融合，同时显著减少制作流程中的人工投入。项目主页：https://aim-uofa.github.io/LivingSwap
英文原文摘要 1372 字符
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