标题： DyTact：捕捉手-物操作中的动态接触 显示英文标题

标题： DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation

摘要： 在人工智能角色动画、扩展现实（XR）和机器人领域中，重建动态手-物接触对于实现逼真的操作至关重要，但由于严重的遮挡、复杂的表面细节以及现有捕捉技术的局限性，这一问题仍然极具挑战性。 在本文中，我们提出了DyTact，这是一种无标记捕捉方法，能够在非侵入性的方式下准确捕捉手-物操作中的动态接触。 我们的方法基于二维高斯Surfel（表面元素）的动态、关节表示来建模复杂的操作。 通过将这些Surfel绑定到MANO网格上，DyTact利用模板模型的归纳偏置来稳定并加速优化过程。 一个精炼模块解决了与时间相关的高频变形问题，而一种基于接触引导的自适应采样策略则选择性地在接触区域增加Surfel密度，以应对严重的遮挡。 广泛的实验表明，DyTact不仅实现了最先进的动态接触估计精度，还显著提高了新视角合成的质量，同时保持了快速优化和高效内存使用。 项目页面：https://oliver-cong02.github.io/DyTact.github.io/ 。 显示英文摘要

摘要： 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/ .