Vid2Haircut: Reconstruction 3D Strand-Based Hairstyles from Video
We present Vid2Haircut, a novel approach for strand-based 3D hair reconstruction from monocular head-motion videos. While existing multi-view methods achieve high-fidelity results, they require controlled capture setups. In contrast, single-image approaches suffer from occlusion-driven ambiguities, particularly in unseen regions such as the back of the head. Recent monocular video methods improve reconstruction by leveraging learned priors, but may struggle under natural head motion. To address this, our approach reconstructs accurate geometry from a short monocular video by leveraging viewpoint variations induced by natural head motion to resolve occlusions. Specifically, we extend the learned hair prior Im2Hair by jointly optimizing a shared, scalp-aligned hair map in a canonical space across multiple key-frames. To accommodate hair motion during capture, we incorporate a deformation MLP that predicts residual strand offsets, preventing frame-specific deformations from corrupting the canonical hairstyle. Additionally, we stabilize the reconstruction of poorly observed regions using visibility-aware updates and neighboring-strand smoothness constraints. Experiments on synthetic and real data demonstrate improved back-view consistency, scalp attachment, and overall reconstruction quality compared to state-of-the-art baselines, while requiring only casual monocular video as input.
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