HairCS: Reconstructing Strand-Based Hair from Hair Cards

Zixuan Lu, Tongtong Wang, Yuefan Shen, Zhongtian Zheng, Chenfanfu Jiang +2 more
9/15/2026
cs.GRcs.CV

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Abstract

We present an automated pipeline that converts hair-card models into high-quality strand-based hairstyles. Given a collection of textured triangular or quad strips as input, our method produces a strand-based representation that preserves the original hairstyle while enriching it with fine-scale geometric detail and adhering to standard production requirements: strands originate from the scalp, roots are uniformly distributed, and the hair volume is plausibly filled. The resulting assets are directly compatible with strand-based rendering, physics-based simulation, and common grooming modifiers (e.g., clumping, curling, noise) for enhanced realism and artistic control. We validate our approach on a large and diverse set of hairstyles, including short and long hair, curly styles, and complex styles such as buns and ponytails.

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Cite this paper

@article{lu2026haircs,
  title  = {HairCS: Reconstructing Strand-Based Hair from Hair Cards},
  author = {Zixuan Lu and Tongtong Wang and Yuefan Shen and Zhongtian Zheng and Chenfanfu Jiang and Yin Yang and Kui Wu},
  year   = {2026},
  eprint = {2609.16465},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2609.16465v1}
}

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