MASS：基于网格-椭圆对齐的可变形Surfel散射法用于从第一人称单目视频中重建和渲染手部
MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video
AI总结：
本文提出MASS方法，通过可变形2D高斯Surfel表示解决单目视频中高保真3D手部重建难题，结合网格对齐Steiner椭圆和分形密集化提升分辨率，引入高斯Surfel变形和两阶段训练策略提升重建质量。
摘要：
从第一人称单目视频重建高保真的3D手部仍面临挑战，由于高分辨率几何、手-物体交互和手部复杂物体的捕捉限制。此外，现有方法常导致高计算成本，难以实现实时应用。本文提出Mesh-inellipse Aligned deformable Surfel Splatting (MASS) 来解决这些问题，通过可变形2D高斯Surfel表示。我们引入网格对齐的Steiner椭圆和分形密集化，用于网格到Surfel的转换，从而从粗参数化手部网格启动高分辨率2D高斯Surfel，提供具有逼真渲染潜力的表面表示。第二，我们提出高斯Surfel变形，通过预测残差更新到Surfel属性并引入不透明度掩码，以高效建模手部变形和个性化特征，无需自适应密度控制。此外，我们提出双阶段训练策略和新的绑定损失，以提高优化鲁棒性和重建质量。在ARCTIC数据集、Hand Appearance数据集和Interhand2.6M数据集上的大量实验表明，我们的模型在重建性能上优于现有最先进方法。
英文摘要：
Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge due to the limitations in capturing high-resolution geometry, hand-object interactions, and complex objects on hands. Additionally, existing methods often incur high computational costs, making them impractical for real-time applications. In this work, we propose Mesh-inellipse Aligned deformable Surfel Splatting (MASS) to address these challenges by leveraging a deformable 2D Gaussian Surfel representation. We introduce the mesh-aligned Steiner Inellipse and fractal densification for mesh-to-surfel conversion that initiates high-resolution 2D Gaussian surfels from coarse parametric hand meshes, providing surface representation with photorealistic rendering potential. Second, we propose Gaussian Surfel Deformation, which enables efficient modeling of hand deformations and personalized features by predicting residual updates to surfel attributes and introducing an opacity mask to refine geometry and texture without adaptive density control. In addition, we propose a two-stage training strategy and a novel binding loss to improve the optimization robustness and reconstruction quality. Extensive experiments on the ARCTIC dataset, the Hand Appearance dataset, and the Interhand2.6M dataset demonstrate that our model achieves superior reconstruction performance compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
