GAMA-Pose: Graph-Aware Multi-Representation Aggregation for 3D Human Pose Estimation

Monocular 3D human pose estimation presents a considerable challenge owing to the intrinsic depth ambiguity associated with single-camera observations. Existing methods primarily rely on mean per joint position error (MPJPE) loss to train models for the conversion from 2D to 3D coordinates. However, empirical analysis reveals that models trained solely with point-based supervision may produce biomechanically implausible poses or exhibit significant depth ambiguity, even when achieving low MPJPE. This limitation arises from the fact that point-based loss only considers individual joint locations without accounting for inter-joint relationships. Fortunately, edges of human pose encode critical prior knowledge, including skeleton connectivity and biomechanical distributions. Explicitly modeling edge representations enables the model to overcome the constraints associated with point-only approaches, reducing the uncertainty in the optimization process of the 2D-3D inverse mapping and directly constraining depth ambiguity. Therefore, we propose the Graph-Aware Multi-Representation Aggregation (GAMA-Pose) framework that jointly predicts points and edges, with their fusion serving as the final output. To ensure the accuracy of edge predictions and mitigate depth ambiguity, Anti-Depth-Ambiguity Loss (ADA-Loss) is introduced to supervise the properties of edges and give direct supervision on depth ambiguity. Correspondingly, edge-based metrics are proposed to quantify the error of predicted edges. Experiments conducted on Human3.6M and MPI-INF-3DHP datasets demonstrate that GAMA-Pose effectively addresses the limitations of models relying solely on point constraints, mitigates depth ambiguity, enhances the accuracy of both point and edge predictions, and achieves state-of-the-art (SOTA) performance on both datasets.