Estimating 3D human poses from 2D images remains challenging due to occlusions and projective ambiguity. Multi-view learning-based approaches mitigate these issues but often fail to generalize to real-world scenarios, as large-scale multi-view datasets with 3D ground truth are scarce and captured under constrained conditions. To overcome this limitation, recent methods rely on 2D pose estimation combined with 2D-to-3D pose lifting trained on synthetic data. Building on our previous MPL framework, we propose RUMPL, a transformer-based 3D pose lifter that introduces a 3D ray-based representation of 2D keypoints. This formulation enables a model agnostic to camera parameters that can be universally deployed across arbitrary camera configurations in a given area without retraining or fine-tuning. A new View Fusion Transformer leverages learned fused-ray tokens to aggregate information along rays, further improving multi-view consistency. Evaluation on standard benchmarks shows that RUMPL significantly outperforms existing methods, yielding a 56.6% MPJPE (All KP) reduction on Human3.6M over triangulation-based methods and exceeding 70% improvement on the CMU Panoptic dataset when compared to transformer-based image-representation approaches. Results on new benchmarks, including in-the-wild multi-view and multi-person datasets, confirm its robustness and scalability.