The hand plays a pivotal role in human ability to grasp and manipulate objects and controllable grasp synthesis is the key for successfully performing downstream tasks. Existing methods that use human intention or task-level language as control signals for grasping inherently face ambiguity. To address this challenge, we propose a grasp synthesis method guided by text prompts of object grasping parts, Text2Grasp, which provides more precise control. Specifically, we present a two-stage method that includes a text-guided diffusion model TextGraspDiff to first generate a coarse grasp pose, then apply a hand-object contact optimization process to ensure both plausibility and diversity. Furthermore, by leveraging Large Language Model, our method facilitates grasp synthesis guided by task-level and personalized text descriptions without additional manual annotations. Extensive experiments demonstrate that our method achieves not only accurate part-level grasp control but also comparable performance in grasp quality.

以对象抓取部分的文本提示为引导，提出了一种文本引导的抓取合成方法，该方法包括一个文本引导的扩散模型 TextGraspDiff 和一个手-物体接触优化过程，以确保合理性和多样性，实验结果验证了该方法在精确的部位级别抓取控制和抓取质量方面的可比性。

Text2Grasp: 文本提示下的物体抓取部位的抓取合成