HairFormer: Transformer-Based Dynamic Neural Hair Simulation

2025-07-16

AI Summary

Hair dynamics simulation faces significant generalization challenges across diverse hairstyles, body morphologies, and motion types. This paper introduces the first Transformer-based two-stage neural framework: a static network employs a Transformer to predict initial, penetration-free hair coverage geometry; a dynamic network incorporates cross-attention to jointly encode static geometry and motion inputs, generating high-fidelity secondary motion sequences. By pioneering the integration of Transformers into hair simulation—augmented with physics-aware loss functions—the method substantially improves generalization and stability on unseen long-hair configurations and abrupt motions. The approach enables real-time static inference and dynamic sequence generation, preserving fine hair strand details, eliminating body penetration, and delivering high-quality, multi-hairstyle dynamic simulation under arbitrary poses.

Technology Category

• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Simulating hair dynamics across diverse hairstyles and motions
• Resolving hair-body penetrations while preserving hair fidelity
• Generating expressive dynamics and complex secondary motions efficiently

Innovation

• Transformer-based static network resolves penetrations
• Dynamic network uses cross-attention for expressive motions
• Real-time inference for static and dynamic draping