Gestures that accompany speech are an essential part of natural and efficient embodied
human communication. The automatic generation of such co‐speech gestures is a long …

Large datasets are the cornerstone of recent advances in computer vision using deep
learning. In contrast, existing human motion capture (mocap) datasets are small and the …

Training computers to understand, model, and synthesize human gras** requires a rich
dataset containing complex 3D object shapes, detailed contact information, hand pose and …

We present a system for real-time hand-tracking to drive virtual and augmented reality
(VR/AR) experiences. Using four fisheye monochrome cameras, our system generates …

To build general robotic agents that can operate in many environments, it is often imperative
for the robot to collect experience in the real world. However, this is often not feasible due to …

Physical contact between hands and objects plays a critical role in human grasps. We show
that optimizing the pose of a hand to achieve expected contact with an object can improve …

Natural hand manipulations exhibit complex finger maneuvers adaptive to object shapes
and the tasks at hand. Learning dexterous manipulation from data in a brute force way …

Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning
skills, intuition, and creativity to perform tasks. However, teleoperation solutions for high …

We propose a stretch-sensing soft glove to interactively capture hand poses with high
accuracy and without requiring an external optical setup. We demonstrate how our device …

In virtual reality, the lack of kinesthetic feedback often prevents users from experiencing the
weight of virtual objects. Control-to-display (C/D) ratio manipulation has been proposed as a …