CJRR-Net: A Candidate-Joint Reprojection Ranking for Future-View Selection in Human Pose Estimation

Huiting Xi, Marsha Mariya Kappan, Eduardo Benitez Sandoval, Francisco Cruz

Multi-camera systems support performance recording, sports and human-movement analysis, where selecting an informative viewpoint is important for reliable human pose estimation. We propose the Candidate-Joint Reprojection Ranking Network (CJRR-Net) for future-view selection among eight calibrated cameras in MPI-INF-3DHP. The model combines current-view pose observations, camera configuration, and candidate-specific geometric features obtained by reprojecting the estimated 3D pose and its uncertainty into each candidate view. CJRR-Net achieves a selected-view 2D mean per-joint position error (MPJPE) of 105.18 pixels, compared with 114.64 pixels for the shared-input MLP and 117.56 pixels for adapted PoseErrNet. It also achieves the lowest mean error among the listed policies in each evaluated occlusion group. A blender rollout illustrates repeated camera selection. These findings support candidate-specific geometry for single-view selection in the evaluated fixed eight-camera setting.