Hand-Object Contact Consistency Reasoning for Human Grasps Generation (2104.03304v1)

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/

• The paper introduces a dual-network method combining GraspCVAE and ContactNet to generate realistic 3D human grasps with natural hand-object contact consistency.
• It devises novel hand- and object-centric loss functions that enhance physical plausibility by reducing penetration and aligning contact regions.
• Self-supervised test-time adaptation improves generalization on unseen objects, promising advances in virtual reality and robotic manipulation.

Hand-Object Contact Consistency Reasoning for Human Grasps Generation

The paper "Hand-Object Contact Consistency Reasoning for Human Grasps Generation" addresses a challenging aspect of human-computer interaction: generating realistic 3D human grasps for various objects. The research focuses on modeling hand-object interaction by simulating natural grasp patterns using advanced generative models, which is a significant departure from the relatively simplified robotic grasps typically studied with parallel jaw grippers.

Key Contributions

The research introduces a paradigm for enhancing the realism of synthetic human grasps through a dual-network approach that emphasizes the importance of hand-object contact consistency. The authors develop two neural networks: GraspCVAE, a Conditional Variational Auto-Encoder for grasp generation, and ContactNet, a network for predicting object contact maps. These networks are trained with novel objectives that prioritize the consistency between hand contact points and object contact regions, which is critical to ensure both physical plausibility and natural appearance.

1. GraspCVAE Network: Utilizes a multi-faceted loss function to train a generative model that encodes first-person interactions, leveraging data from both hand and object features. The conditional generative network outputs the hand grasp pose, parametrized by the MANO model, by learning from large-scale datasets that capture diverse grasp scenarios.
2. ContactNet Network: This network estimates the contact map on the object point cloud, ensuring that the hand grasp aligns with typical contact regions learned from empirical data. Importantly, it provides a target for test-time optimization, serving as a self-supervised signal that refines the generated grasp.
3. Training Objectives: Two novel losses are introduced — a hand-centric loss that encourages the hand contact vertices to approach the object and an object-centric loss that ensures common contact regions on objects are appropriately covered by the hand grasp. These losses facilitate a mutual agreement between hand and object interactions.
4. Test-Time Adaptation: An innovative self-supervised mechanism optimizes hand-object interaction during inference, allowing the generative model to adapt grasps for unseen objects, thereby significantly improving generalization.

Experimental Results

The experiments conducted on datasets such as Obman, HO-3D, and FPHA illustrate remarkable performance improvements over previous state-of-the-art methods. For instance, the proposed approach decreases penetration depth and volume — key metrics for physical plausibility — while improving grasp displacement and perceptual quality. Through quantitative analysis, it is evident that introducing contact consistency yields substantial gains, particularly in scenarios involving out-of-domain objects, a testament to the robustness afforded by the self-supervised adaptation during test time.

Implications and Future Work

The paper presents promising implications for fields such as virtual reality and robotic manipulation, emphasizing the need for realistic and stable human-object interaction modeling. The methodological advancements offer the potential to broaden applications in simulation environments where human-like hand articulation is critical.

Future research could explore adaptive mechanisms to further bridge the domain gap between simulated and real-world interactions. Additionally, extending this approach to incorporate full-body human interaction modeling could enhance the fidelity of simulations in complex environments. Enhanced training strategies and datasets may also contribute to richer feature extraction, propelling the capabilities of generative models in realistic motion synthesis.

In summary, this paper contributes significantly to the domain of human grasp synthesis by integrating hand-object contact reasoning into the framework of generative modeling, underscoring the importance of nuanced interaction paradigms for achieving enhanced realism in simulated environments.