HiFiVFS: High Fidelity Video Face Swapping

• URL: http://arxiv.org/abs/2411.18293v2
• Date: Tue, 10 Dec 2024 11:13:57 GMT
• Title: HiFiVFS: High Fidelity Video Face Swapping
• Authors: Xu Chen, Keke He, Junwei Zhu, Yanhao Ge, Wei Li, Chengjie Wang,
• Abstract summary: Face swapping aims to generate results that combine the identity from the source with attributes from the target.<n>We propose a high fidelity video face swapping framework, which leverages the strong generative capability and temporal prior of Stable Video Diffusion.<n>Our method achieves state-of-the-art (SOTA) in video face swapping, both qualitatively and quantitatively.
• Score: 35.49571526968986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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