VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping

• URL: http://arxiv.org/abs/2602.07835v1
• Date: Sun, 08 Feb 2026 06:13:19 GMT
• Title: VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping
• Authors: Sanoojan Baliah, Yohan Abeysinghe, Rusiru Thushara, Khan Muhammad, Abhinav Dhall, Karthik Nandakumar, Muhammad Haris Khan,
• Abstract summary: VFace is a training-free, plug-and-play method for high-quality face swapping in videos.<n>It can be seamlessly integrated with image-based face swapping approaches built on diffusion models.<n>Our method significantly enhances temporal consistency and visual fidelity.
• Score: 48.76390632712573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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