EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion

• URL: http://arxiv.org/abs/2604.05794v1
• Date: Tue, 07 Apr 2026 12:30:19 GMT
• Title: EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion
• Abstract summary: Implicit neural representations can capture the global hair shape but often fail to preserve fine-grained strand details.<n>We propose EfficientMonoHair, a framework that combines implicit neural network with geometric fusion for strand-level reconstruction from monocular video.<n>Our method achieves quality comparable to state-of-the-art methods, while improving runtime efficiency by nearly an order of magnitude.
• Score: 11.985280856105076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Strand-level hair geometry reconstruction is a fundamental problem in virtual human modeling and the digitization of hairstyles. However, existing methods still suffer from a significant trade-off between accuracy and efficiency. Implicit neural representations can capture the global hair shape but often fail to preserve fine-grained strand details, while explicit optimization-based approaches achieve high-fidelity reconstructions at the cost of heavy computation and poor scalability. To address this issue, we propose EfficientMonoHair, a fast and accurate framework that combines the implicit neural network with multi-view geometric fusion for strand-level reconstruction from monocular video. Our method introduces a fusion-patch-based multi-view optimization that reduces the number of optimization iterations for point cloud direction, as well as a novel parallel hair-growing strategy that relaxes voxel occupancy constraints, allowing large-scale strand tracing to remain stable and robust even under inaccurate or noisy orientation fields. Extensive experiments on representative real-world hairstyles demonstrate that our method can robustly reconstruct high-fidelity strand geometries with accuracy. On synthetic benchmarks, our method achieves reconstruction quality comparable to state-of-the-art methods, while improving runtime efficiency by nearly an order of magnitude.

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