REACH: Hand Pose Estimation from Room Corners

TL;DR

This paper presents REACH-Net, a Transformer-based model for accurate 3D hand pose estimation from low-resolution, occluded views in room corners, leveraging hand-body coordination and a new large-scale dataset.

Contribution

The introduction of a novel Transformer-based model and the REACH dataset for accurate 3D hand pose estimation from afar in challenging room environments.

Findings

REACH-Net outperforms existing methods in accuracy.

The REACH dataset includes diverse daily activities of 50 participants.

The approach enables 'in-the-wild' human behavior analysis.

Abstract

We introduce a novel 3D hand pose estimator that can accurately recover the shape and pose of people's hands in a room from afar, typically from fixed cameras at room corners, in extremely low-resolution and frequently occluded views. Our key idea is to fully leverage hand-body coordination, its temporal progression, and multiview observations. We achieve this with a novel Transformer-based model, in which hand and body configurations are modeled through correlations between their visual features expressed as per-view tokens, and their temporal coordination is exploited in an autoregressive manner. We introduce a novel dataset, which we refer to as REACH, Room-Environment dataset Annotated with Chest cameras for Hand pose estimation, to train and test our method. REACH is a first-of-its-kind large-scale hand pose dataset that captures accurate hand movements of 50 participants across a wide variety of daily activities. In order to avoid interfering with natural movements while annotating the hands with accurate shape and pose, we leverage concealed chest cameras. Through extensive experiments, including comparative studies with existing methods, we show that our model, REACH-Net, achieves highly accurate 3D hand pose estimation from afar. These results broaden the horizon of 3D hand pose estimation, especially towards "in-the-wild" continuous human behavior analysis.