In this paper, we consider the problem of 2D human pose estimation on stereo image pairs. In particular, we aim at estimating the location, orientation and scale of upper-body parts of people detected in stereo image pairs from realistic stereo videos that can be found in the Internet. To address this task, we propose a novel pictorial structure model to exploit the stereo information included in such stereo image pairs: the Stereo Pictorial Structure (SPS). To validate our proposed model, we contribute a new annotated dataset of stereo image pairs, the Stereo Human Pose Estimation Dataset (SHPED), obtained from YouTube stereoscopic video sequences, depicting people in challenging poses and diverse indoor and outdoor scenarios. The experimental results on SHPED indicates that SPS improves on state-ofthe-art monocular models thanks to the appropriate use of the stereo information.

This work targets 2D articulated human pose estimation (i.e. localization of body limbs) in stereo videos. Although in recent years depth-based devices (e.g. Microsoft Kinect) have gained popularity, as they perform very well in controlled indoor environments (e.g. living rooms, operating theatres or gyms), they suffer clear problems in outdoor scenarios and, therefore, human pose estimation is still an interesting unsolved problem. We propose here a novel approach that is able to localize upper-body keypoints (i.e. shoulders, elbows, wrists) in temporal sequences of stereo image pairs. Our method starts by locating and segmenting people in the image pairs by using disparity and appearance information. Then, a set of candidate body poses is computed for each view independently. Finally, temporal and stereo consistency is applied to estimate a final 2D pose. We validate our model on three challenging datasets: "Stereo Human Pose Estimation Dataset", "Poses in the Wild" and "INRIA 3DMovie". The experimental results show that our model not only establishes new state-of-the-art results on stereo sequences, but also brings improvements in monocular sequences.