  In this paper we present a novel approach for bottom-up multi-person 3D human
pose estimation from monocular RGB images. We propose to use high resolution
volumetric heatmaps to model joint locations, devising a simple and effective
compression method to drastically reduce the size of this representation. At
the core of the proposed method lies our Volumetric Heatmap Autoencoder, a
fully-convolutional network tasked with the compression of ground-truth
heatmaps into a dense intermediate representation. A second model, the Code
Predictor, is then trained to predict these codes, which can be decompressed at
test time to re-obtain the original representation. Our experimental evaluation
shows that our method performs favorably when compared to state of the art on
both multi-person and single-person 3D human pose estimation datasets and,
thanks to our novel compression strategy, can process full-HD images at the
constant runtime of 8 fps regardless of the number of subjects in the scene.
Code and models available at https://github.com/fabbrimatteo/LoCO .
