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.

我们介绍了HARPER，这是一个针对Boston Dynamics制造的四足机器人Spot与用户之间的二人互动中，3D人体姿势估计和预测的新型数据集。重点的创新在于关注机器人的视角，即机器人传感器捕捉的数据，这使得3D人体姿势分析具有挑战性。该数据集包括15个动作，其中10个涉及机器人与用户之间的物理接触。数据集不仅包括Spot内置立体摄像头的录像，还包括6个相机的OptiTrack系统的录像（所有录像均同步）。这导致了具有毫米级精度的地面真实的骨骼表示。此外，该数据集还包括基于公开可用的基线方法的可复现的3D人体姿势估计、人体姿势预测和碰撞预测基准。这使得未来的HARPER用户可以与我们在这项工作中提供的结果进行严格的比较。

从机器人的角度探索三维人体姿势估计和预测：HARPER数据集