SPHEAR：用于完整统计式3D建模的球形头部配准
SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling
AI总结：
本研究提出基于球形嵌入配准的可微统计3D头部模型SPHEAR，突破经典非刚性配准范式，支持多维度头部数据生成，在效率与精度上均优于现有方法。
摘要：
我们提出了SPHEAR，这是一种精准、可微的参数化统计3D人体头部模型，由一种基于球形嵌入的新型3D配准方法实现。我们转变了经典非刚性配准方法的范式，这类方法需在多种表面先验条件下运行，我们的方法提升了重建保真度，同时最大限度减少了所需的人工干预。此外，SPHEAR是一个“完整”模型，不仅能采样多样化的合成头部形状与面部表情，还可生成注视方向、高分辨率彩色纹理、表面法线贴图，以及以发丝细节呈现的发型。SPHEAR可用于自动生成真实感视觉数据、语义标注以及通用重建任务。与当前最优方法相比，我们的组件速度更快且内存效率更高，实验验证了我们设计选择的合理性，以及配准、重建与生成技术的准确性。
英文摘要：
We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings. We shift the paradigm away from the classical Non-Rigid Registration methods, which operate under various surface priors, increasing reconstruction fidelity and minimizing required human intervention. Additionally, SPHEAR is a \emph{complete} model that allows not only to sample diverse synthetic head shapes and facial expressions, but also gaze directions, high-resolution color textures, surface normal maps, and hair cuts represented in detail, as strands. SPHEAR can be used for automatic realistic visual data generation, semantic annotation, and general reconstruction tasks. Compared to state-of-the-art approaches, our components are fast and memory efficient, and experiments support the validity of our design choices and the accuracy of registration, reconstruction and generation techniques.
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