  We address the problem of generalizability for multi-view 3D human pose
estimation. The standard approach is to first detect 2D keypoints in images and
then apply triangulation from multiple views. Even though the existing methods
achieve remarkably accurate 3D pose estimation on public benchmarks, most of
them are limited to a single spatial camera arrangement and their number.
Several methods address this limitation but demonstrate significantly degraded
performance on novel views. We propose a stochastic framework for human pose
triangulation and demonstrate a superior generalization across different camera
arrangements on two public datasets. In addition, we apply the same approach to
the fundamental matrix estimation problem, showing that the proposed method can
successfully apply to other computer vision problems. The stochastic framework
achieves more than 8.8% improvement on the 3D pose estimation task, compared to
the state-of-the-art, and more than 30% improvement for fundamental matrix
estimation, compared to a standard algorithm.
