What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Building effective imitation learning methods that enable robots to learn from limited data
and still generalize across diverse real-world environments is a long-standing problem in …

We study the problem of teaching humanoid robots manipulation skills by imitating from
single video demonstrations. We introduce OKAMI, a method that generates a manipulation …

We introduce DiffH2O, a new diffusion-based framework for synthesizing realistic, dexterous
hand-object interactions from natural language. Our model employs a temporal two-stage …

Imitation learning from human hand motion data presents a promising avenue for imbuing
robots with human-like dexterity in real-world manipulation tasks. Despite this potential …

Abstract 3D hand pose estimation in everyday egocentric images is challenging for several
reasons: poor visual signal (occlusion from the object of interaction, low resolution & motion …

Humans can learn to manipulate new objects by simply watching others; providing robots
with the ability to learn from such demonstrations would enable a natural interface specifying …

3D Hand reconstruction from a single RGB image is challenging due to the articulated
motion, self-occlusion, and interaction with objects. Existing SOTA methods employ attention …

Existing datasets for 3D hand-object interaction are limited either in the data cardinality, data
variations in interaction scenarios, or the quality of annotations. In this work, we present a …

In recent years, 3D hand pose estimation methods have garnered significant attention due to
their extensive applications in human-computer interaction, virtual reality, and robotics. In …