  This paper proposes a statistical approach to 2D pose estimation from human
images. The main problems with the standard supervised approach, which is based
on a deep recognition (image-to-pose) model, are that it often yields
anatomically implausible poses, and its performance is limited by the amount of
paired data. To solve these problems, we propose a semi-supervised method that
can make effective use of images with and without pose annotations.
Specifically, we formulate a hierarchical generative model of poses and images
by integrating a deep generative model of poses from pose features with that of
images from poses and image features. We then introduce a deep recognition
model that infers poses from images. Given images as observed data, these
models can be trained jointly in a hierarchical variational autoencoding
(image-to-pose-to-feature-to-pose-to-image) manner. The results of experiments
show that the proposed reflective architecture makes estimated poses
anatomically plausible, and the performance of pose estimation improved by
integrating the recognition and generative models and also by feeding
non-annotated images.
