  We address the challenges in estimating 3D human poses from multiple views
under occlusion and with limited overlapping views. We approach multi-view,
single-person 3D human pose reconstruction as a regression problem and propose
a novel encoder-decoder Transformer architecture to estimate 3D poses from
multi-view 2D pose sequences. The encoder refines 2D skeleton joints detected
across different views and times, fusing multi-view and temporal information
through global self-attention. We enhance the encoder by incorporating a
geometry-biased attention mechanism, effectively leveraging geometric
relationships between views. Additionally, we use detection scores provided by
the 2D pose detector to further guide the encoder's attention based on the
reliability of the 2D detections. The decoder subsequently regresses the 3D
pose sequence from these refined tokens, using pre-defined queries for each
joint. To enhance the generalization of our method to unseen scenes and improve
resilience to missing joints, we implement strategies including scene
centering, synthetic views, and token dropout. We conduct extensive experiments
on three benchmark public datasets, Human3.6M, CMU Panoptic and
Occlusion-Persons. Our results demonstrate the efficacy of our approach,
particularly in occluded scenes and when few views are available, which are
traditionally challenging scenarios for triangulation-based methods.
