StableGrasp: Reconstructing Physically Stable Human Hand Grasps from Single Images

Reconstructing a physically stable human grasp from a single RGB image is challenging because physically modeling grasps is itself difficult, and the problem requires recovering not only a visually constrained hand pose but also the underlying physical hand control. 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, without sacrificing the visual and geometric quality of the reconstruction. When projected back onto the input image, the grasp appears visually accurate. Experiments show that our approach produces far more stable grasps than alternative hand-control strategies and can also benefit visual-only grasp reconstruction pipelines by turning their outputs into physically stable grasps.