  Automatically determining three-dimensional human pose from monocular RGB
image data is a challenging problem. The two-dimensional nature of the input
results in intrinsic ambiguities which make inferring depth particularly
difficult. Recently, researchers have demonstrated that the flexible
statistical modelling capabilities of deep neural networks are sufficient to
make such inferences with reasonable accuracy. However, many of these models
use coordinate output techniques which are memory-intensive, not
differentiable, and/or do not spatially generalise well. We propose
improvements to 3D coordinate prediction which avoid the aforementioned
undesirable traits by predicting 2D marginal heatmaps under an augmented
soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent
heatmaps whilst maintaining differentiability. We are also able to achieve
state-of-the-art accuracy on publicly available 3D human pose estimation data.
