Unsupervised 3D Braided Hair Reconstruction from a Single-View Image

TL;DR

This paper introduces an unsupervised method for reconstructing detailed 3D braided hairstyles from single images, using a synthetic braid model inspired by braid theory, outperforming existing approaches in accuracy and realism.

Contribution

The paper presents a novel unsupervised pipeline that effectively captures complex braided structures from single images, advancing 3D hair reconstruction techniques.

Findings

Outperforms state-of-the-art methods in accuracy and realism

Efficiently reconstructs complex braided hairstyles from single images

Supports expressive hairstyle modeling in digital humans

Abstract

Reconstructing 3D braided hairstyles from single-view images remains a challenging task due to the intricate interwoven structure and complex topologies of braids. Existing strand-based hair reconstruction methods typically focus on loose hairstyles and often struggle to capture the fine-grained geometry of braided hair. In this paper, we propose a novel unsupervised pipeline for efficiently reconstructing 3D braided hair from single-view RGB images. Leveraging a synthetic braid model inspired by braid theory, our approach effectively captures the complex intertwined structures of braids. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches, providing superior accuracy, realism, and efficiency in reconstructing 3D braided hairstyles, supporting expressive hairstyle modeling in digital humans.