CVSep 1, 2025

Im2Haircut: Single-view Strand-based Hair Reconstruction for Human Avatars

ETH Zurich
arXiv:2509.01469v16 citationsh-index: 21
Originality Incremental advance
AI Analysis

This addresses the problem of realistic hair simulation for human avatars, which is incremental as it builds on prior hairstyle priors but improves data usage.

The paper tackles 3D hair reconstruction from single photographs by combining a global hair prior with local optimization, achieving superior performance in capturing detailed hair orientation, overall silhouette, and backside consistency compared to existing methods.

We present a novel approach for 3D hair reconstruction from single photographs based on a global hair prior combined with local optimization. Capturing strand-based hair geometry from single photographs is challenging due to the variety and geometric complexity of hairstyles and the lack of ground truth training data. Classical reconstruction methods like multi-view stereo only reconstruct the visible hair strands, missing the inner structure of hairstyles and hampering realistic hair simulation. To address this, existing methods leverage hairstyle priors trained on synthetic data. Such data, however, is limited in both quantity and quality since it requires manual work from skilled artists to model the 3D hairstyles and create near-photorealistic renderings. To address this, we propose a novel approach that uses both, real and synthetic data to learn an effective hairstyle prior. Specifically, we train a transformer-based prior model on synthetic data to obtain knowledge of the internal hairstyle geometry and introduce real data in the learning process to model the outer structure. This training scheme is able to model the visible hair strands depicted in an input image, while preserving the general 3D structure of hairstyles. We exploit this prior to create a Gaussian-splatting-based reconstruction method that creates hairstyles from one or more images. Qualitative and quantitative comparisons with existing reconstruction pipelines demonstrate the effectiveness and superior performance of our method for capturing detailed hair orientation, overall silhouette, and backside consistency. For additional results and code, please refer to https://im2haircut.is.tue.mpg.de.

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