Reviews: Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction

This paper focuses on the problem of 3D human pose reconstruction from multiple viewpoints in video sequences. The system assumes that a set of 2D human poses are available (e.g. by using OpenPose), coming from different camera viewpoints, and the paper proposes a pipeline for simultaneously selecting the next viewpoint that would be needed to decrease the reconstruction error and generating the 3D reconstruction. An artificial agent, named ACTOR, is defined for the purpose of selecting the camera viewpoints, following a reward-based approach. The proposed system is evaluated on the CMU Panoptic dataset, which contains 480 VGA camera views. The experimental results show that by using the proposed agent the reconstruction error is better than using the baseline methods. The remaining components of the system do not seem to be novel.