Hand-Object Contact Consistency Reasoning for Human Grasps Generation
arXiv:2104.03304
Abstract
While predicting robot grasps with parallel jaw grippers have been well studied and widely applied in robot manipulation tasks, the study on natural human grasp generation with a multi-finger hand remains a very challenging problem. In this paper, we propose to generate human grasps given a 3D object in the world. Our key observation is that it is crucial to model the consistency between the hand contact points and object contact regions. That is, we encourage the prior hand contact points to be close to the object surface and the object common contact regions to be touched by the hand at the same time. Based on the hand-object contact consistency, we design novel objectives in training the human grasp generation model and also a new self-supervised task which allows the grasp generation network to be adjusted even during test time. Our experiments show significant improvement in human grasp generation over state-of-the-art approaches by a large margin. More interestingly, by optimizing the model during test time with the self-supervised task, it helps achieve larger gain on unseen and out-of-domain objects. Project page: https://hwjiang1510.github.io/GraspTTA/
Project page: https://hwjiang1510.github.io/GraspTTA/
References in corpus (8)
- Auto-Encoding Variational Bayes
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Embodied Hands: Modeling and Capturing Hands and Bodies Together
- Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
- Domain Adaptation for Visual Applications: A Comprehensive Survey
- SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping
- Geometric Pose Affordance: 3D Human Pose with Scene Constraints
- Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes