To facilitate the analysis of human actions, interactions and emotions, we compute a 3D
model of human body pose, hand pose, and facial expression from a single monocular …

Abstract Analysis of hand-hand interactions is a crucial step towards better understanding
human behavior. However, most researches in 3D hand pose estimation have focused on …

To understand how people look, interact, or perform tasks, we need to quickly and
accurately capture their 3D body, face, and hands together from an RGB image. Most …

This work addresses a novel and challenging problem of estimating the full 3D hand shape
and pose from a single RGB image. Most current methods in 3D hand analysis from …

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 …

Most of the existing deep learning-based methods for 3D hand and human pose estimation
from a single depth map are based on a common framework that takes a 2D depth map and …

We present a comprehensive framework for egocentric interaction recognition using
markerless 3D annotations of two hands manipulating objects. To this end, we propose a …

In this work we study the use of 3D hand poses to recognize first-person dynamic hand
actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences …

We introduce a simple and effective network architecture for monocular 3D hand pose
estimation consisting of an image encoder followed by a mesh convolutional decoder that is …

We present a unified framework for understanding 3D hand and object interactions in raw
image sequences from egocentric RGB cameras. Given a single RGB image, our model …