无需标记捕捉手物交互中的动态接触，精度高且速度快。
DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation
- 用2D高斯面元建模手物动态接触，绑定到MANO网格提升稳定性。
- 在接触区域自适应加密采样，有效应对遮挡与高频形变。
- 适合动画、XR和机器人领域，兼具高精度与高效计算。
重建动态手物接触对人工智能角色动画、扩展现实（XR）和机器人技术至关重要，但因严重遮挡、复杂表面细节及现有捕获技术局限而极具挑战。本文提出DyTact，一种无需标记的非侵入式动态接触捕获方法。该方法基于2D高斯面元构建动态可变形表示，通过将面元绑定至MANO网格，利用模板模型的归纳偏置稳定并加速优化。引入精修模块处理随时间变化的高频形变，同时设计接触引导的自适应采样策略，在接触区域选择性增加面元密度以应对严重遮挡。大量实验表明，DyTact不仅在动态接触估计上达到当前最优精度，还显著提升新视角合成质量，且具备快速优化与低内存开销优势。
原文摘要 · Abstract (English)
Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains challenging due to heavy occlusions, complex surface details, and limitations in existing capture techniques. In this paper, we introduce DyTact, a markerless capture method for accurately capturing dynamic contact in hand-object manipulations in a non-intrusive manner. Our approach leverages a dynamic, articulated representation based on 2D Gaussian surfels to model complex manipulations. By binding these surfels to MANO meshes, DyTact harnesses the inductive bias of template models to stabilize and accelerate optimization. A refinement module addresses time-dependent high-frequency deformations, while a contact-guided adaptive sampling strategy selectively increases surfel density in contact regions to handle heavy occlusion. Extensive experiments demonstrate that DyTact not only achieves state-of-the-art dynamic contact estimation accuracy but also significantly improves novel view synthesis quality, all while operating with fast optimization and efficient memory usage. Project Page: https://oliver-cong02.github.io/DyTact.github.io/ .
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