Auto Hair Card Extraction for Smooth Hair with Differentiable Rendering

TL;DR

This paper introduces an automated, differentiable rendering-based pipeline for converting strand-based hair models into efficient, high-quality hair card models suitable for real-time applications, reducing manual effort.

Contribution

It presents a novel differentiable representation and a two-stage optimization algorithm for automatic hair card generation from strand models, improving efficiency and quality.

Findings

Effective on diverse hairstyles including curly and coily hair.

Supports seamless level-of-detail transitions with texture sharing.

Achieves high visual fidelity with fewer textures and cards.

Abstract

Hair cards remain a widely used representation for hair modeling in real-time applications, offering a practical trade-off between visual fidelity, memory usage, and performance. However, generating high-quality hair card models remains a challenging and labor-intensive task. This work presents an automated pipeline for converting strand-based hair models into hair card models with a limited number of cards and textures while preserving the hairstyle appearance. Our key idea is a novel differentiable representation where each strand is encoded as a projected 2D spline in the texture space, which enables efficient optimization with differentiable rendering and structured results respecting the hair geometry. Based on this representation, we develop a novel algorithm pipeline, where we first cluster hair strands into initial hair cards and project the strands into the texture space. We then conduct a two-stage optimization where our first stage optimizes the texture and geometry of each hair card separately, and after texture reduction, our second stage conducts joint optimization of all the cards for fine-tuning. Put together, our method is evaluated on a wide range of hairstyles, including straight, wavy, curly, and coily hairs. To better capture the appearance of short or coily hair, we additionally support hair cap and cross-card. Furthermore, our framework supports seamless LoD transitions via texture sharing, balancing texture memory efficiency and visual quality.