UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues
This addresses robust pose estimation for applications like motion capture, though it appears incremental by building on existing frameworks.
The paper tackles 3D human pose estimation by introducing UPose3D, which improves accuracy and scalability without 3D annotations, achieving state-of-the-art performance in out-of-distribution settings and competitive results with 2D supervision only.
We introduce UPose3D, a novel approach for multi-view 3D human pose estimation, addressing challenges in accuracy and scalability. Our method advances existing pose estimation frameworks by improving robustness and flexibility without requiring direct 3D annotations. At the core of our method, a pose compiler module refines predictions from a 2D keypoints estimator that operates on a single image by leveraging temporal and cross-view information. Our novel cross-view fusion strategy is scalable to any number of cameras, while our synthetic data generation strategy ensures generalization across diverse actors, scenes, and viewpoints. Finally, UPose3D leverages the prediction uncertainty of both the 2D keypoint estimator and the pose compiler module. This provides robustness to outliers and noisy data, resulting in state-of-the-art performance in out-of-distribution settings. In addition, for in-distribution settings, UPose3D yields performance rivalling methods that rely on 3D annotated data while being the state-of-the-art among methods relying only on 2D supervision.