  Most recent approaches to monocular 3D human pose estimation rely on Deep
Learning. They typically involve regressing from an image to either 3D joint
coordinates directly or 2D joint locations from which 3D coordinates are
inferred. Both approaches have their strengths and weaknesses and we therefore
propose a novel architecture designed to deliver the best of both worlds by
performing both simultaneously and fusing the information along the way. At the
heart of our framework is a trainable fusion scheme that learns how to fuse the
information optimally instead of being hand-designed. This yields significant
improvements upon the state-of-the-art on standard 3D human pose estimation
benchmarks.
