Multimodal 3D Human Pose Estimation from a Single Image

In this paper, we propose a method for estimating 3D human pose from a single RGB image. Compared to methods that either provide point estimates for coordinate regression or unimodal predictions of joint locations, our approach predicts joint locations using multimodal distributions. In addition, we apply a data-driven approach to learn the conditional dependencies of the relative positions of joints. Our end-to-end approach takes as input images with either 2D or 3D labels and performs on par or better than the state-of-the-art on the Human3.6M and MPII datasets.