发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单图像人手抓取物理稳定性不足的问题，提出StableGrasp可微仿真优化框架，分离视觉姿态与控制目标，联合优化几何与控制，生成更稳定且视觉一致的抓取。
AI 中文摘要
从单张RGB图像重建物理稳定的人手抓取具有挑战性，因为对抓取进行物理建模本身就很困难，并且该问题不仅需要估计视觉约束的手部姿态，还需要估计稳定抓取的控制目标。现有方法要么仅建模视觉手部几何而不考虑物理，要么依赖不太合理的物理建模，这限制了所得抓取的物理有效性。在本文中，我们提出StableGrasp，一个基于可微仿真的优化框架，明确地将视觉手部姿态与决定抓取力的控制目标分离。我们的方法通过最小化可微仿真器中的抓取动能来联合优化手部几何和控制，同时正则化手部几何以保持视觉一致性和几何合理性。重建的抓取在严格的物理仿真下显著更稳定，同时与输入图像保持视觉一致且几何合理。实验表明，我们的方法比替代的手部控制策略产生更稳定的抓取，通过将视觉仅抓取重建管道的输出转化为物理稳定的抓取而使其受益。
英文摘要
Reconstructing a physically stable human grasp from a single RGB image is challenging because physically modeling grasps is itself difficult, and the problem requires estimating not only a visually constrained hand pose but also a control target that stabilizes the grasp. Existing methods either model only visual hand geometry without considering physics, or rely on less plausible physical modeling, which limits the physical validity of the resulting grasps. In this paper, we present StableGrasp, a differentiable simulation-based optimization framework that explicitly separates the visual hand pose from the control target that determines the grasping forces. Our method jointly optimizes hand geometry and control by minimizing the kinetic energy of the grasp in a differentiable simulator, while regularizing the hand geometry to preserve visual consistency and geometric plausibility. The reconstructed grasps are substantially more stable under rigorous physical simulation, while remaining visually consistent with the input images and geometrically plausible. Experiments show that our approach produces far more stable grasps than alternative hand-control strategies, benefiting visual-only grasp reconstruction pipelines by turning their outputs into physically stable grasps.
