  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.
