ActionPose: Pretraining 3D Human Pose Estimation with the Dark Knowledge of Action

2D-to-3D human pose lifting is an ill-posed problem due to depth ambiguity and occlusion. Existing methods relying on spatial and temporal consistency alone are insufficient to resolve these problems because they lack semantic information of the motions. To overcome this, we propose ActionPose, a framework that leverages action knowledge by aligning motion embeddings with text embeddings of fine-grained action labels. ActionPose operates in two stages: pretraining and fine-tuning. In the pretraining stage, the model learns to recognize actions and reconstruct 3D poses from masked and noisy 2D poses. During the fine-tuning stage, the model is further refined using real-world 3D human pose estimation datasets without action labels. Additionally, our framework incorporates masked body parts and masked time windows in motion modeling to mitigate the effects of ambiguous boundaries between actions in both temporal and spatial domains. Experiments demonstrate the effectiveness of ActionPose, achieving state-of-the-art performance in 3D pose estimation on public datasets, including Human3.6M and MPI-INF-3DHP. Specifically, ActionPose achieves an MPJPE of 36.7mm on Human3.6M with detected 2D poses as input and 15.5mm on MPI-INF-3DHP with ground-truth 2D poses as input.