TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints

• URL: http://arxiv.org/abs/2502.06392v1
• Date: Mon, 10 Feb 2025 12:26:02 GMT
• Title: TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints
• Authors: Pengyu Long, Zijun Zhao, Min Ouyang, Qingcheng Zhao, Qixuan Zhang, Wei Yang, Lan Xu, Jingyi Yu,
• Abstract summary: Existing text or image-guided generation methods fail to handle the richness and complexity of diverse styles. We present TANGLED, a novel approach for 3D hair strand generation that accommodates diverse image inputs across styles, viewpoints, and quantities of input views.
• Score: 38.95048174663582
• License:
• Abstract: Hairstyles are intricate and culturally significant with various geometries, textures, and structures. Existing text or image-guided generation methods fail to handle the richness and complexity of diverse styles. We present TANGLED, a novel approach for 3D hair strand generation that accommodates diverse image inputs across styles, viewpoints, and quantities of input views. TANGLED employs a three-step pipeline. First, our MultiHair Dataset provides 457 diverse hairstyles annotated with 74 attributes, emphasizing complex and culturally significant styles to improve model generalization. Second, we propose a diffusion framework conditioned on multi-view linearts that can capture topological cues (e.g., strand density and parting lines) while filtering out noise. By leveraging a latent diffusion model with cross-attention on lineart features, our method achieves flexible and robust 3D hair generation across diverse input conditions. Third, a parametric post-processing module enforces braid-specific constraints to maintain coherence in complex structures. This framework not only advances hairstyle realism and diversity but also enables culturally inclusive digital avatars and novel applications like sketch-based 3D strand editing for animation and augmented reality.

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