3D Hand Pose Detection in Egocentric RGB-D Images

TL;DR

This paper presents a novel approach for estimating hand poses from egocentric RGB-D images, leveraging synthetic training data and priors to overcome occlusions and limited viewpoints, achieving state-of-the-art results.

Contribution

The authors introduce a discriminative tracking-by-detection framework using synthetic data and priors for improved egocentric hand pose estimation from RGB-D images.

Findings

Achieves state-of-the-art accuracy in hand detection and pose estimation.

Utilizes synthetic egocentric scene models for training.

Outperforms existing methods on a real dataset.

Abstract

We focus on the task of everyday hand pose estimation from egocentric viewpoints. For this task, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his/her environment. Despite the recent advances in full-body pose estimation using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D images is still an unsolved problem. The problem is considerably exacerbated when analyzing hands performing daily activities from a first-person viewpoint, due to severe occlusions arising from object manipulations and a limited field-of-view. Our system addresses these difficulties by exploiting strong priors over viewpoint and pose in a discriminative tracking-by-detection framework. Our priors are operationalized through a photorealistic synthetic model of egocentric scenes, which is used to generate training data for learning depth-based pose classifiers. We evaluate our approach on an annotated dataset of real egocentric object manipulation scenes and compare to both commercial and academic approaches. Our method provides state-of-the-art performance for both hand detection and pose estimation in egocentric RGB-D images.