  We present two novel solutions for multi-view 3D human pose estimation based
on new learnable triangulation methods that combine 3D information from
multiple 2D views. The first (baseline) solution is a basic differentiable
algebraic triangulation with an addition of confidence weights estimated from
the input images. The second solution is based on a novel method of volumetric
aggregation from intermediate 2D backbone feature maps. The aggregated volume
is then refined via 3D convolutions that produce final 3D joint heatmaps and
allow modelling a human pose prior. Crucially, both approaches are end-to-end
differentiable, which allows us to directly optimize the target metric. We
demonstrate transferability of the solutions across datasets and considerably
improve the multi-view state of the art on the Human3.6M dataset. Video
demonstration, annotations and additional materials will be posted on our
project page (https://saic-violet.github.io/learnable-triangulation).
