# StableGrasp: Reconstructing Physically Stable Human Hand Grasps from Single Images

arXiv: https://arxiv.org/abs/2610.09195 · HTML: https://mlsanity.com/paper/2610.09195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Builds on (most cited first, 17 of 17)

- [H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions](https://mlsanity.com/paper/1904.05349/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands](https://mlsanity.com/paper/2208.12250/) (2022)
- [EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild](https://mlsanity.com/paper/2411.14280/) (2024)
- [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024)
- [HO-3D_v3: Improving the Accuracy of Hand-Object Annotations of the HO-3D Dataset](https://mlsanity.com/paper/2107.00887/) (2021)
- [DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness](https://mlsanity.com/paper/2503.08257/) (2025)
- [Reconstructing Hand-Held Objects in 3D from Images and Videos](https://mlsanity.com/paper/2404.06507/) (2024)
- [DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field](https://mlsanity.com/paper/2308.08231/) (2023)
- [CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation](https://mlsanity.com/paper/2308.10574/) (2023)
- [FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric](https://mlsanity.com/paper/2302.13687/) (2023)
- [Physics-aware Hand-object Interaction Denoising](https://mlsanity.com/paper/2405.11481/) (2024)
- [Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach](https://mlsanity.com/paper/2402.18897/) (2024)
- [Task-Oriented Dexterous Hand Pose Synthesis Using Differentiable Grasp Wrench Boundary Estimator](https://mlsanity.com/paper/2309.13586/) (2023)
- [D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction](https://mlsanity.com/paper/2311.14189/) (2023)
- [Grasp-and-Lift: Executable 3D Hand-Object Interaction Reconstruction via Physics-in-the-Loop Optimization](https://mlsanity.com/paper/2601.18121/) (2026)

## Abstract (arXiv)

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.