ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models

TL;DR

ClickDiff introduces a novel controllable grasp generation model that uses semantic contact maps to produce realistic and precise hand-object interactions, enhancing the accuracy and controllability of generated grasps.

Contribution

The paper presents ClickDiff, a new model that leverages semantic contact maps and a dual generation framework for controllable and accurate grasp synthesis.

Findings

Effective in both unimanual and bimanual grasp generation

Demonstrates robustness on unseen objects

Outperforms existing methods in realism and controllability

Abstract

Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and diversity under scene constraints, they tend to overlook the fine-grained hand-object interactions such as contacts, resulting in inaccurate and undesired grasps. To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM). Particularly when synthesizing interactive grasps, the method enables the precise control of grasp synthesis through either user-specified or algorithmically predicted Semantic Contact Map. Specifically, to optimally utilize contact supervision constraints and to accurately model the complex physical structure of hands, we propose a Dual Generation Framework. Within this framework, the Semantic Conditional Module generates reasonable contact maps based on fine-grained contact information, while the Contact Conditional Module utilizes contact maps alongside object point clouds to generate realistic grasps. We evaluate the evaluation criteria applicable to controllable grasp generation. Both unimanual and bimanual generation experiments on GRAB and ARCTIC datasets verify the validity of our proposed method, demonstrating the efficacy and robustness of ClickDiff, even with previously unseen objects. Our code is available at https://github.com/adventurer-w/ClickDiff.