Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
arxiv(2024)
摘要
We introduce HARPER, a novel dataset for 3D body pose estimation and forecast
in dyadic interactions between users and , 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.
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