  Monocular 3D human pose estimation technologies have the potential to greatly
increase the availability of human movement data. The best-performing models
for single-image 2D-3D lifting use graph convolutional networks (GCNs) that
typically require some manual input to define the relationships between
different body joints. We propose a novel transformer-based approach that uses
the more generalised self-attention mechanism to learn these relationships
within a sequence of tokens representing joints. We find that the use of
intermediate supervision, as well as residual connections between the stacked
encoders benefits performance. We also suggest that using error prediction as
part of a multi-task learning framework improves performance by allowing the
network to compensate for its confidence level. We perform extensive ablation
studies to show that each of our contributions increases performance.
Furthermore, we show that our approach outperforms the recent state of the art
for single-frame 3D human pose estimation by a large margin. Our code and
trained models are made publicly available on Github.
