DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation

TL;DR

DeepSimHO introduces a deep-learning pipeline that integrates physics simulation and neural network-based gradient approximation to improve the stability and efficiency of 3D hand-object pose estimation from a single image.

Contribution

It presents a novel approach combining physics simulation with neural networks to evaluate and refine hand-object poses, addressing stability issues in pose estimation.

Findings

Significantly improves stability of estimated poses

Achieves superior efficiency over test-time optimization

Effectively approximates physics simulation gradients

Abstract

This paper addresses the task of 3D pose estimation for a hand interacting with an object from a single image observation. When modeling hand-object interaction, previous works mainly exploit proximity cues, while overlooking the dynamical nature that the hand must stably grasp the object to counteract gravity and thus preventing the object from slipping or falling. These works fail to leverage dynamical constraints in the estimation and consequently often produce unstable results. Meanwhile, refining unstable configurations with physics-based reasoning remains challenging, both by the complexity of contact dynamics and by the lack of effective and efficient physics inference in the data-driven learning framework. To address both issues, we present DeepSimHO: a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Specifically, for an initial hand-object pose estimated by a base network, we forward it to a physics simulator to evaluate its stability. However, due to non-smooth contact geometry and penetration, existing differentiable simulators can not provide reliable state gradient. To remedy this, we further introduce a deep network to learn the stability evaluation process from the simulator, while smoothly approximating its gradient and thus enabling effective back-propagation. Extensive experiments show that our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization. The code is available at https://github.com/rongakowang/DeepSimHO.