Reviews: MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

Authors compare their method to a very recent state-of-the-art method and use the same evaluation protocol. They present detailed evaluation of how synthetically generated data fairs against real data and how using them both together improves performance. Additionally authors demonstrate the advantage of formulating 3D pose estimation as a classification problem over direct regression to joint coordinates. This is facilitated by a large number of training samples, which they show is crucial for the classification CNN: in the small data regime full body regression actually outperforms 3D pose classification. However, authors only evaluate their method using evaluation protocol described in [17, 40].