3D pictorial structures for human pose estimation with supervoxels

Pictorial structures provide a powerful framework for human pose estimation, in particular in the domain of 2D data. However, solving pictorial structures directly in 3D drastically increases its complexity and it quickly exceeds tractable dimensions. In this paper, we propose a discretization-by-segmentation approach by applying supervoxels to 3D pictorial structures which significantly reduces the search space. The proposed 3D pictorial structures approach achieves 3D errors of 115 mm and 135 mm on the HumanEva-I and UMPM datasets and PCP scores of 78% and 75%, respectively. Due to the search space reduction, the overall pose estimation runtime is below 100 ms which is up to four orders of magnitude faster than comparable 3D pictorial structure approaches. The presented approach is not limited to human pose estimation, but provides a general and efficient solution for 3D pictorial structures.