We present an unsupervised learning approach to re- cover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi- view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. Dur- ing training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new ‘synthetic’ 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can de- fine self-consistency loss both in 3D and in 2D. The training can thus be self supervised by exploiting the geometric self- consistency of the lift-reproject-lift process. We show that self-consistency alone is not sufficient to generate realistic skeletons, however adding a 2D pose discriminator enables the lifter to output valid 3D poses. Additionally, to learn from 2D poses ‘in the wild’, we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. This improves results and demonstrates the useful- ness of 2D pose data for unsupervised 3D lifting. Results on Human3.6M dataset for 3D human pose estimation demon- strate that our approach improves upon the previous un- supervised methods by 30% and outperforms many weakly supervised approaches that explicitly use 3D data.

We explore 3D human pose estimation from a single RGB image. While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions. Our approach is based on two key observations (1) Deep neural nets have revolutionized 2D pose estimation, producing accurate 2D predictions even for poses with self-occlusions (2) Big-datasets of 3D mocap data are now readily available, making it tempting to lift predicted 2D poses to 3D through simple memorization (e.g., nearest neighbors). The resulting architecture is straightforward to implement with off-the-shelf 2D pose estimation systems and 3D mocap libraries. Importantly, we demonstratethatsuchmethodsoutperformalmostallstate-of-theart 3D pose estimation systems, most of which directly try to regress 3D pose from 2D measurements.

Video-based group behavior analysis is drawing attention to its rich applications in sports, military, surveillance and biological observations. The recent advances in tracking techniques, based on either computer vision methodology or hardware sensors, further provide the opportunity of better solving this challenging task. Focusing specifically on the analysis of basketball offensive strategies, we introduce a systematic approach to establishing unsupervised modeling of group behaviors. In view that a possible group behavior (offensive strategy) could be of different duration and represented by dynamic player trajectories, the crux of our method is to automatically divide training data into meaningful clusters and learn their respective spatio-temporal model, which is established upon Gaussian mixture regression to account for intra-class spatio-temporal variations. The resulting strategy representation turns out to be flexible that can be used to not only establish the discriminant functions but also improve learning the models. We demonstrate the usefulness of our approach by exploring its effectiveness in analyzing a set of given basketball video clips.