The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

Generating digital humans that move realistically has many applications and is widely
studied, but existing methods focus on the major limbs of the body, ignoring the hands and …

Estimating 3D hand and object pose from a single image is an extremely challenging
problem: hands and objects are often self-occluded during interactions, and the 3D …

Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but
also pays attention to the contact due to their interaction. Significant progress has been …

We propose to perform imitation learning for dexterous manipulation with multi-finger robot
hand from human demonstrations, and transfer the policy to the real robot hand. We …

With the increasing demand for the dexterity of robotic operation, dexterous manipulation of
multi-fingered robotic hands with reinforcement learning is an interesting subject in the field …

We present ArtiGrasp, a novel method to synthesize bimanual hand-object interactions that
include grasping and articulation. This task is challenging due to the diversity of the global …

We propose a physics-based method for synthesizing dexterous hand-object interactions in
a full-body setting. While recent advancements have addressed specific facets of human …

The existing pose estimation approaches often require a large number of annotated images
to attain good estimation performance, which are laborious to acquire. To reduce the human …

Accurately estimating 3D hand pose is crucial for understanding how humans interact with
the world. Despite remarkable progress, existing methods often struggle to generate …