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Many real-world applications require the estimation of human body joints forhigher-level tasks as, for example, human behaviour understanding. In recentyears, depth sensors have become a popular approach to obtain three-dimensionalinformation. The depth maps generated by these sensors provide information thatcan be employed to disambiguate the poses observed in two-dimensional images.This work addresses the problem of 3D human pose estimation from depth mapsemploying a Deep Learning approach. We propose a model, named Deep Depth Pose(DDP), which receives a depth map containing a person and a set of predefined3D prototype poses and returns the 3D position of the body joints of theperson. In particular, DDP is defined as a ConvNet that computes the specificweights needed to linearly combine the prototypes for the given input. We havethoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, whichrespectively depict realistic and synthetic samples, defining a newstate-of-the-art on them.

3D human pose estimation from depth maps using a deep combination of poses