Evaluating 3D Human Pose Estimation in Occluded Multi-Sensor Scenarios: Dataset and Annotation Approach

Obtaining ground truth annotations for 3D pose estimation (3D HPE) typically depends on motion capture equipment (Mocap), which is not only expensive but impractical for widespread deployment. In contrast, triangulation can reconstruct 3D poses solely from multi-view 2D poses with known camera parameters, eliminating the need for Mocap. However, inherent noise in 2D pose predictions introduces uncertainties, compromising the reliability of the results. To obtain more reliable annotations with noisy input, we introduce an annotation approach for the 3D HPE task, driven by prior knowledge of the skeletal configuration. We split our approach into two steps: first a parametric model is designed to enhance confidence predictions. Then, a differentiable weighted triangulation is employed to estimate the 3D pose in world space, leveraging the predicted confidence scores as weights. The pipeline is trained using a bone length loss. Moreover, we collect a multi-view dataset for 3D HPE and annotate it using our proposed annotation tool. This dataset is characterized by more construction scenarios, including heavier occlusion cases, diverse viewing directions, and the integration of various optical sensors, setting it apart from existing datasets. Experiments on both our dataset and Human3.6M demonstrate the effectiveness of our method.