Title: Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation,
Abstract: We propose a novel approach to 3D human pose estimation from a single depth
map. Recently, convolutional neural network (CNN) has become a powerful
paradigm in computer vision. Many of computer vision tasks have benefited from
CNNs, however, the conventional approach to directly regress 3D body joint
locations from an image does not yield a noticeably improved performance. In
contrast, we formulate the problem as estimating per-voxel likelihood of key
body joints from a 3D occupancy grid. We argue that learning a mapping from
volumetric input to volumetric output with 3D convolution consistently improves
the accuracy when compared to learning a regression from depth map to 3D joint
coordinates. We propose a two-stage approach to reduce the computational
overhead caused by volumetric representation and 3D convolution: Holistic 2D
prediction and Local 3D prediction. In the first stage, Planimetric Network
(P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D
space. In the second stage, Volumetric Network (V-Net) estimates the per-voxel
likelihood of each body joints in the local 3D space around the 2D estimations
of the first stage, effectively reducing the computational cost. Our model
outperforms existing methods by a large margin in publicly available datasets.