  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.
