@inproceedings{
chen2024doubly,
title={Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation},
author={Yunlu Chen and Francisco Vicente Carrasco and Christian H{\"a}ne and Giljoo Nam and Jean-Charles Bazin and Fernando De la Torre},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h34jVnPo1c}
}
We introduce a doubly hierarchical generative representation for strand-based hair geometry that progresses from coarse, low-pass filtered guide hair to densely populated hair strands rich in high-frequency details. We employ the Discrete Cosine Transform (DCT) to separate low-frequency structural curves from high-frequency curliness and noise, avoiding the Gibbs' oscillation issues associated with the standard Fourier transform in open curves. Unlike the guide hair sampled from the scalp UV map grids which may lose capturing details of the hairstyle in existing methods, our method samples optimal sparse guide strands by utilizing $k$-medoids clustering centres from low-pass filtered dense strands, which more accurately retain the hairstyle's inherent characteristics. The proposed variational autoencoder-based generation network, with an architecture inspired by geometric deep learning and implicit neural representations, facilitates flexible, off-the-grid guide strand modelling and enables the completion of dense strands in any quantity and density, drawing on principles from implicit neural representations. Empirical evaluations confirm the capacity of the model to generate convincing guide hair and dense strands, complete with nuanced high-frequency details.
Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation
Yunlu Chen Francisco Vicente Carrasco Christian Häne Giljoo Nam Jean-Charles Bazin Fernando De la Torre