VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910026491232256 |
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| author | Baliah, Sanoojan Abeysinghe, Yohan Thushara, Rusiru Muhammad, Khan Dhall, Abhinav Nandakumar, Karthik Khan, Muhammad Haris |
| author_facet | Baliah, Sanoojan Abeysinghe, Yohan Thushara, Rusiru Muhammad, Khan Dhall, Abhinav Nandakumar, Karthik Khan, Muhammad Haris |
| contents | We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches built on diffusion models. First, we introduce a Frequency Spectrum Attention Interpolation technique to facilitate generation and intact key identity characteristics. Second, we achieve Target Structure Guidance via plug-and-play attention injection to better align the structural features from the target frame to the generation. Third, we present a Flow-Guided Attention Temporal Smoothening mechanism that enforces spatiotemporal coherence without modifying the underlying diffusion model to reduce temporal inconsistencies typically encountered in frame-wise generation. Our method requires no additional training or video-specific fine-tuning. Extensive experiments show that our method significantly enhances temporal consistency and visual fidelity, offering a practical and modular solution for video-based face swapping. Our code is available at https://github.com/Sanoojan/VFace. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07835 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping Baliah, Sanoojan Abeysinghe, Yohan Thushara, Rusiru Muhammad, Khan Dhall, Abhinav Nandakumar, Karthik Khan, Muhammad Haris Computer Vision and Pattern Recognition We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches built on diffusion models. First, we introduce a Frequency Spectrum Attention Interpolation technique to facilitate generation and intact key identity characteristics. Second, we achieve Target Structure Guidance via plug-and-play attention injection to better align the structural features from the target frame to the generation. Third, we present a Flow-Guided Attention Temporal Smoothening mechanism that enforces spatiotemporal coherence without modifying the underlying diffusion model to reduce temporal inconsistencies typically encountered in frame-wise generation. Our method requires no additional training or video-specific fine-tuning. Extensive experiments show that our method significantly enhances temporal consistency and visual fidelity, offering a practical and modular solution for video-based face swapping. Our code is available at https://github.com/Sanoojan/VFace. |
| title | VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.07835 |