  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.
