  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.
