HairShifter: Consistent and High-Fidelity Video Hair Transfer via Anchor-Guided Animation

• URL: http://arxiv.org/abs/2507.12758v1
• Date: Thu, 17 Jul 2025 03:22:39 GMT
• Title: HairShifter: Consistent and High-Fidelity Video Hair Transfer via Anchor-Guided Animation
• Authors: Wangzheng Shi, Yinglin Zheng, Yuxin Lin, Jianmin Bao, Ming Zeng, Dong Chen,
• Abstract summary: HairShifter is a novel framework that unifies high-quality image hair transfer with smooth and coherent video animation.<n>Our method maintains hairstyle fidelity across frames while preserving non-hair regions.<n>HairShifter achieves state-of-the-art performance in video hairstyle transfer, combining superior visual quality, temporal consistency, and scalability.
• Score: 29.404225620335193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hair transfer is increasingly valuable across domains such as social media, gaming, advertising, and entertainment. While significant progress has been made in single-image hair transfer, video-based hair transfer remains challenging due to the need for temporal consistency, spatial fidelity, and dynamic adaptability. In this work, we propose HairShifter, a novel "Anchor Frame + Animation" framework that unifies high-quality image hair transfer with smooth and coherent video animation. At its core, HairShifter integrates a Image Hair Transfer (IHT) module for precise per-frame transformation and a Multi-Scale Gated SPADE Decoder to ensure seamless spatial blending and temporal coherence. Our method maintains hairstyle fidelity across frames while preserving non-hair regions. Extensive experiments demonstrate that HairShifter achieves state-of-the-art performance in video hairstyle transfer, combining superior visual quality, temporal consistency, and scalability. The code will be publicly available. We believe this work will open new avenues for video-based hairstyle transfer and establish a robust baseline in this field.

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