  Generating realistic human grasps is a crucial yet challenging task for
applications involving object manipulation in computer graphics and robotics.
Existing methods often struggle with generating fine-grained realistic human
grasps that ensure all fingers effectively interact with objects, as they focus
on encoding hand with the whole representation and then estimating both hand
posture and position in a single step. In this paper, we propose a novel
Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE) to address this
limitation by decomposing hand into several distinct parts and encoding them
separately. This part-aware decomposed architecture facilitates more precise
management of the interaction between each component of hand and object,
enhancing the overall reality of generated human grasps. Furthermore, we design
a newly dual-stage decoding strategy, by first determining the type of grasping
under skeletal physical constraints, and then identifying the location of the
grasp, which can greatly improve the verisimilitude as well as adaptability of
the model to unseen hand-object interaction. In experiments, our model achieved
about 14.1% relative improvement in the quality index compared to the
state-of-the-art methods in four widely-adopted benchmarks. Our source code is
available at https://github.com/florasion/D-VQVAE.
