Grasping Field: Learning Implicit Representations for Human Grasps (2008.04451v3)

Abstract: Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods.

• The paper introduces the Grasping Field, a novel deep learning framework that uses implicit representations to synthesize realistic human-object interactions.
• The method maps 3D space to a 2D interaction domain using signed distance functions within a VAE framework to reduce interpenetration and enhance grasp stability.
• Experimental evaluations, including physics simulations and user studies, confirm the approach’s competitive performance and its potential in robotics and virtual reality applications.

Grasping Field: Learning Implicit Representations for Human Grasps

The paper "Grasping Field: Learning Implicit Representations for Human Grasps" proposes a novel approach to model and synthesize realistic human hand grasps in interaction with objects. The central concept introduced is the "Grasping Field," which leverages implicit representations learned through deep neural networks to model the three-dimensional (3D) interaction space between human hands and objects. The Grasping Field is designed to overcome the challenges posed by the high degrees of freedom of human hands, the necessity of conforming to object surfaces, and the requirement of physical plausibility during hand-object interaction.

Methodological Overview

The methodology is rooted in characterizing each point in 3D space by its signed distances to the surfaces of the hand and object. This conceptualization allows the mapping from 3D space to a 2-dimensional (2D) space where interactions are naturally and explicitly modeled. The hand, object, and contact regions are represented by implicit surfaces, facilitating an efficient representation that can be integrated with neural networks.

The architecture utilizes a generative model capable of synthesizing human grasps conditioned solely on a 3D object point cloud. Training leverages a variational autoencoder (VAE) framework, where a deep network parameterizes the Grasping Field. This model is evaluated against a baseline that predicts hand parameters using explicit hand templates, demonstrating superior physical and perceptual realism in synthesized grasps.

Numerical and Qualitative Results

Evaluations are conducted using several metrics: intersection volume and depth to measure interpenetration, contact ratio of samples, and grasp stability evaluated through physics simulations. The model shows reduced interpenetration volumes and improved contact ratios, indicating a significant advancement in generating physically plausible grasps. User studies further confirm the perceptual quality of the generated grasps, with results comparable to or exceeding natural human grasp examples in certain datasets.

In the domain of 3D hand-object reconstruction, the Grasping Field demonstrates competitive performance, particularly in reducing intersection errors and improving contact realism compared to state-of-the-art mesh-based methods. The approach supports generalization across diverse datasets with varying object characteristics, which is validated through cross-dataset evaluations, maintaining robustness even when tested with unseen objects.

Theoretical and Practical Implications

The Grasping Field introduces an efficient method for modeling hand-object interactions that may inform future work on human grasp synthesis and pose estimation. Theoretically, it contributes to understanding implicit representations in 3D space, serving as a potential bridge between rigid and non-rigid object interaction modeling.

Practically, this approach could enhance applications in robotics where realistic hand-object interactions are critical, such as automated manipulation and human-robot collaboration. Additionally, the synthesized grasps could find uses in virtual and augmented reality environments, providing more natural and interactive user experiences.

Future Directions

Future research could focus on integrating semantic object understanding and dynamic hand manipulations to improve the contextual grasp synthesis. Furthermore, extending the approach to consider temporal aspects could enable synthesis of continuous hand-object interactions, vital for robotics and animation.

In conclusion, this paper marks a notable step towards synthesizing realistic hand-object interactions by leveraging implicit representations. The Grasping Field provides a framework that offers both theoretical depth and practical utility, facilitating advances in artificial intelligence and robotics.