Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers

TL;DR

This paper introduces a novel task-oriented human grasp synthesis method that leverages scene- and task-aware contact maps, significantly improving grasp quality and task alignment over existing approaches.

Contribution

We propose a two-stage pipeline using task-aware contact maps for more accurate and context-sensitive human grasp synthesis, along with a new dataset and evaluation metric.

Findings

Enhanced grasp accuracy with scene and task integration

Significant improvements over existing methods

Validated effectiveness through experiments

Abstract

In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose a two-stage pipeline that first constructs a task-aware contact map informed by the scene and task. In the subsequent stage, we use this contact map to synthesize task-oriented human grasps. We introduce a new dataset and a metric for the proposed task to evaluate our approach. Our experiments validate the importance of modeling both scene and task, demonstrating significant improvements over existing methods in both grasp quality and task performance. See our project page for more details: https://hcis-lab.github.io/TOHGS/