  Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task,
as a 2D pose in an image might originate from different possible 3D poses. Yet,
most 3D-HPE methods rely on regression models, which assume a one-to-one
mapping between inputs and outputs. In this work, we provide theoretical and
empirical evidence that, because of this ambiguity, common regression models
are bound to predict topologically inconsistent poses, and that traditional
evaluation metrics, such as the MPJPE, P-MPJPE and PCK, are insufficient to
assess this aspect. As a solution, we propose ManiPose, a novel
manifold-constrained multi-hypothesis model capable of proposing multiple
candidate 3D poses for each 2D input, together with their corresponding
plausibility. Unlike previous multi-hypothesis approaches, our solution is
completely supervised and does not rely on complex generative models, thus
greatly facilitating its training and usage. Furthermore, by constraining our
model to lie within the human pose manifold, we can guarantee the consistency
of all hypothetical poses predicted with our approach, which was not possible
in previous works. We illustrate the usefulness of ManiPose in a synthetic
1D-to-2D lifting setting and demonstrate on real-world datasets that it
outperforms state-of-the-art models in pose consistency by a large margin,
while still reaching competitive MPJPE performance.
