Controllable and Expressive One-Shot Video Head Swapping

• URL: http://arxiv.org/abs/2506.16852v1
• Date: Fri, 20 Jun 2025 09:01:17 GMT
• Title: Controllable and Expressive One-Shot Video Head Swapping
• Authors: Chaonan Ji, Jinwei Qi, Peng Zhang, Bang Zhang, Liefeng Bo,
• Abstract summary: We propose a novel diffusion-based multi-condition controllable framework for video head swapping.<n>Our method seamlessly transplants a human head from a static image into a dynamic video, while preserving the original body and background of target video.<n> Experimental results demonstrate that our method excels in seamless background integration while preserving the identity of the source portrait.
• Score: 22.260212663609497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel diffusion-based multi-condition controllable framework for video head swapping, which seamlessly transplant a human head from a static image into a dynamic video, while preserving the original body and background of target video, and further allowing to tweak head expressions and movements during swapping as needed. Existing face-swapping methods mainly focus on localized facial replacement neglecting holistic head morphology, while head-swapping approaches struggling with hairstyle diversity and complex backgrounds, and none of these methods allow users to modify the transplanted head expressions after swapping. To tackle these challenges, our method incorporates several innovative strategies through a unified latent diffusion paradigm. 1) Identity-preserving context fusion: We propose a shape-agnostic mask strategy to explicitly disentangle foreground head identity features from background/body contexts, combining hair enhancement strategy to achieve robust holistic head identity preservation across diverse hair types and complex backgrounds. 2) Expression-aware landmark retargeting and editing: We propose a disentangled 3DMM-driven retargeting module that decouples identity, expression, and head poses, minimizing the impact of original expressions in input images and supporting expression editing. While a scale-aware retargeting strategy is further employed to minimize cross-identity expression distortion for higher transfer precision. Experimental results demonstrate that our method excels in seamless background integration while preserving the identity of the source portrait, as well as showcasing superior expression transfer capabilities applicable to both real and virtual characters.

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