Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset

TL;DR

HARPER is a new dataset capturing 3D human poses from a robot's perspective during dyadic interactions, enabling research on pose estimation, forecasting, and collision prediction from the robot's viewpoint.

Contribution

The paper introduces HARPER, a novel dataset focused on 3D human pose analysis from a robot's perspective, including synchronized multi-camera recordings and benchmarks for various tasks.

Findings

Provides ground-truth skeletal data with sub-millimeter accuracy.

Includes reproducible benchmarks for pose estimation, forecasting, and collision prediction.

Facilitates future research by offering baseline approaches and comprehensive data.

Abstract

We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty is the focus on the robot's perspective, i.e., on the data captured by the robot's sensors. These make 3D body pose analysis challenging because being close to the ground captures humans only partially. The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users. The Corpus contains not only the recordings of the built-in stereo cameras of Spot, but also those of a 6-camera OptiTrack system (all recordings are synchronized). This leads to ground-truth skeletal representations with a precision lower than a millimeter. In addition, the Corpus includes reproducible benchmarks on 3D Human Pose Estimation, Human Pose Forecasting, and Collision Prediction, all based on publicly available baseline approaches. This enables future HARPER users to rigorously compare their results with those we provide in this work.