用视频模型先验提升单图头发三维重建真实感
HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits
- 借视频生成模型的3D先验，把单图重建转为多视角校准任务
- 通过稀疏标注训练神经方向提取器，提升全视角方向估计精度
- 分两阶段生成发丝曲线，兼顾细节与速度，适合虚拟试发场景
从单张图像重建亚毫米级3D头发极为困难，尤其在不可见区域难以保持一致性和真实性。现有方法依赖有限的正面视角信息和小规模、风格受限的合成数据，常在隐含区域表现不佳。本文提出新框架，利用视频生成模型的强大3D先验，将单视图头发重建转化为校准的多视角重建任务。为平衡重建质量与效率，引入基于稀疏真实图像标注训练的神经方向提取器，实现更精准的全视角方向估计。此外，设计基于混合隐式场的两阶段发丝生长算法，在相对快速的速度下合成具有细粒度细节的3D发丝曲线。大量实验表明，本方法在多样化的头发肖像上，于可见与不可见区域均达到单视图3D发丝重建的当前最优性能。
原文摘要 · Abstract (English)
Reconstructing strand-level 3D hair from a single-view image is highly challenging, especially when preserving consistent and realistic attributes in unseen regions. Existing methods rely on limited frontal-view cues and small-scale/style-restricted synthetic data, often failing to produce satisfactory results in invisible regions. In this work, we propose a novel framework that leverages the strong 3D priors of video generation models to transform single-view hair reconstruction into a calibrated multi-view reconstruction task. To balance reconstruction quality and efficiency for the reformulated multi-view task, we further introduce a neural orientation extractor trained on sparse real-image annotations for better full-view orientation estimation. In addition, we design a two-stage strand-growing algorithm based on a hybrid implicit field to synthesize the 3D strand curves with fine-grained details at a relatively fast speed. Extensive experiments demonstrate that our method achieves state-of-the-art performance on single-view 3D hair strand reconstruction on a diverse range of hair portraits in both visible and invisible regions.
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