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This paper proposes a statistical approach to 2D pose estimation from humanimages. The main problems with the standard supervised approach, which is basedon a deep recognition (image-to-pose) model, are that it often yieldsanatomically implausible poses, and its performance is limited by the amount ofpaired data. To solve these problems, we propose a semi-supervised method thatcan make effective use of images with and without pose annotations.Specifically, we formulate a hierarchical generative model of poses and imagesby integrating a deep generative model of poses from pose features with that ofimages from poses and image features. We then introduce a deep recognitionmodel that infers poses from images. Given images as observed data, thesemodels can be trained jointly in a hierarchical variational autoencoding(image-to-pose-to-feature-to-pose-to-image) manner. The results of experimentsshow that the proposed reflective architecture makes estimated posesanatomically plausible, and the performance of pose estimation improved byintegrating the recognition and generative models and also by feedingnon-annotated images.

MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images