CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling

• URL: http://arxiv.org/abs/2509.21114v1
• Date: Thu, 25 Sep 2025 13:00:38 GMT
• Title: CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling
• Authors: Yuze He, Yanning Zhou, Wang Zhao, Jingwen Ye, Yushi Bai, Kaiwen Xiao, Yong-Jin Liu, Zhongqian Sun, Wei Yang,
• Abstract summary: We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling.<n> CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card.<n>Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds.
• Score: 43.54249989719103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using strand-based or volumetric representations, anime hairstyle exhibits highly stylized, piecewise-structured geometry that challenges existing techniques. Existing works often rely on dense mesh modeling or hand-crafted spline curves, making them inefficient for editing and unsuitable for scalable learning. CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card, and each point is encoded with only five geometric parameters. This efficient and accurate representation supports both artist-friendly design and learning-based generation. Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds. By interpreting anime hairstyles as a sequential "hair language", our autoregressive transformer captures both local geometry and global hairstyle topology, resulting in high-fidelity anime hairstyle creation. To facilitate both training and evaluation of anime hairstyle generation, we construct AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with separated hair cards and processed mesh data. Extensive experiments demonstrate state-of-the-art performance of CHARM in both reconstruction accuracy and generation quality, offering an expressive and scalable solution for anime hairstyle modeling. Project page: https://hyzcluster.github.io/charm/

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