Face swapping aims to generate results that combine the identity from the source with attributes from the target. Existing methods primarily focus on image-based face swapping. When processing videos, each frame is handled independently, making it difficult to ensure temporal stability. From a model perspective, face swapping is gradually shifting from generative adversarial networks (GANs) to diffusion models (DMs), as DMs have been shown to possess stronger generative capabilities. Current diffusion-based approaches often employ inpainting techniques, which struggle to preserve fine-grained attributes like lighting and makeup. To address these challenges, we propose a high fidelity video face swapping (HiFiVFS) framework, which leverages the strong generative capability and temporal prior of Stable Video Diffusion (SVD). We build a fine-grained attribute module to extract identity-disentangled and fine-grained attribute features through identity desensitization and adversarial learning. Additionally, We introduce detailed identity injection to further enhance identity similarity. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) in video face swapping, both qualitatively and quantitatively.

本研究针对现有视频换脸技术在处理时间稳定性和细粒度属性保留方面的不足，提出了一种高保真视频换脸框架（HiFiVFS）。该框架利用Stable Video Diffusion的强生成能力和时间先验，结合细粒度属性模块和身份注入方法，显著提高了视频换脸的效果，实现了最先进的性能。