HairFormer: Transformer-Based Dynamic Neural Hair Simulation

• URL: http://arxiv.org/abs/2507.12600v1
• Date: Wed, 16 Jul 2025 19:42:08 GMT
• Title: HairFormer: Transformer-Based Dynamic Neural Hair Simulation
• Authors: Joy Xiaoji Zhang, Jingsen Zhu, Hanyu Chen, Steve Marschner,
• Abstract summary: We propose a Transformer-powered static network that predicts static draped shapes for any hairstyle.<n>A dynamic network with a novel cross-attention mechanism fuses static hair features with kinematic input to generate expressive dynamics.<n>Our method demonstrates high-fidelity and generalizable dynamic hair across various styles, guided by physics-informed losses.
• Score: 3.1157179526391374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating hair dynamics that generalize across arbitrary hairstyles, body shapes, and motions is a critical challenge. Our novel two-stage neural solution is the first to leverage Transformer-based architectures for such a broad generalization. We propose a Transformer-powered static network that predicts static draped shapes for any hairstyle, effectively resolving hair-body penetrations and preserving hair fidelity. Subsequently, a dynamic network with a novel cross-attention mechanism fuses static hair features with kinematic input to generate expressive dynamics and complex secondary motions. This dynamic network also allows for efficient fine-tuning of challenging motion sequences, such as abrupt head movements. Our method offers real-time inference for both static single-frame drapes and dynamic drapes over pose sequences. Our method demonstrates high-fidelity and generalizable dynamic hair across various styles, guided by physics-informed losses, and can resolve penetrations even for complex, unseen long hairstyles, highlighting its broad generalization.

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