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

2025-02-10

AI Summary

Existing 3D hair generation methods struggle to model hairstyle diversity and geometric complexity—particularly for braided styles, cross-cultural aesthetics, and low-quality inputs. To address this, we introduce MultiHair, the first large-scale dataset explicitly designed for hairstyle diversity, and propose a sketch-guided multi-view diffusion framework built upon latent diffusion models (LDMs). Our method integrates multi-view sketch encoding, topology-aware conditional modeling, cross-attention mechanisms, and a parametric braid constraint module. It supports both single- and multi-view image inputs and synthesizes high-fidelity, view- and style-arbitrary 3D hair strand models. Extensive experiments demonstrate that our approach significantly outperforms prior methods in realism, structural consistency, and cultural expressiveness. Notably, it achieves breakthrough performance on complex braided hairstyles and exhibits superior robustness to input degradation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Generate 3D hair strands from diverse images.
• Handle complex hairstyles across various viewpoints.
• Improve realism and diversity in digital avatars.

Innovation

• MultiHair Dataset with diverse hairstyles
• Diffusion framework with multi-view linearts
• Parametric post-processing for complex structures