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/