  Estimating 3D human pose from a single image is a challenging task. This work
attempts to address the uncertainty of lifting the detected 2D joints to the 3D
space by introducing an intermediate state - Part-Centric Heatmap Triplets
(HEMlets), which shortens the gap between the 2D observation and the 3D
interpretation. The HEMlets utilize three joint-heatmaps to represent the
relative depth information of the end-joints for each skeletal body part. In
our approach, a Convolutional Network (ConvNet) is first trained to predict
HEMlests from the input image, followed by a volumetric joint-heatmap
regression. We leverage on the integral operation to extract the joint
locations from the volumetric heatmaps, guaranteeing end-to-end learning.
Despite the simplicity of the network design, the quantitative comparisons show
a significant performance improvement over the best-of-grade method (by 20% on
Human3.6M). The proposed method naturally supports training with "in-the-wild"
images, where only weakly-annotated relative depth information of skeletal
joints is available. This further improves the generalization ability of our
model, as validated by qualitative comparisons on outdoor images.
