TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints
Journal:
arXiv
Published Date:
Feb 10, 2025
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.
