  Dexterous grasping aims to produce diverse grasping postures with a high
grasping success rate. Regression-based methods that directly predict grasping
parameters given the object may achieve a high success rate but often lack
diversity. Generation-based methods that generate grasping postures conditioned
on the object can often produce diverse grasping, but they are insufficient for
high grasping success due to lack of discriminative information. To mitigate,
we introduce a unified diffusion-based dexterous grasp generation model, dubbed
the name UGG, which operates within the object point cloud and hand parameter
spaces. Our all-transformer architecture unifies the information from the
object, the hand, and the contacts, introducing a novel representation of
contact points for improved contact modeling. The flexibility and quality of
our model enable the integration of a lightweight discriminator, benefiting
from simulated discriminative data, which pushes for a high success rate while
preserving high diversity. Beyond grasp generation, our model can also generate
objects based on hand information, offering valuable insights into object
design and studying how the generative model perceives objects. Our model
achieves state-of-the-art dexterous grasping on the large-scale DexGraspNet
dataset while facilitating human-centric object design, marking a significant
advancement in dexterous grasping research. Our project page is
https://jiaxin-lu.github.io/ugg/.
