  In this work, we explore a novel task of generating human grasps based on
single-view scene point clouds, which more accurately mirrors the typical
real-world situation of observing objects from a single viewpoint. Due to the
incompleteness of object point clouds and the presence of numerous scene
points, the generated hand is prone to penetrating into the invisible parts of
the object and the model is easily affected by scene points. Thus, we introduce
S2HGrasp, a framework composed of two key modules: the Global Perception module
that globally perceives partial object point clouds, and the DiffuGrasp module
designed to generate high-quality human grasps based on complex inputs that
include scene points. Additionally, we introduce S2HGD dataset, which comprises
approximately 99,000 single-object single-view scene point clouds of 1,668
unique objects, each annotated with one human grasp. Our extensive experiments
demonstrate that S2HGrasp can not only generate natural human grasps regardless
of scene points, but also effectively prevent penetration between the hand and
invisible parts of the object. Moreover, our model showcases strong
generalization capability when applied to unseen objects. Our code and dataset
are available at https://github.com/iSEE-Laboratory/S2HGrasp.
