2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we intro-duce a novel benchmark “MPII Human Pose”1 that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future develop-ments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities [1]. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activ-ities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each im-age we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we per-form a detailed analysis of leading human pose estimation approaches and gaining insights for the success and fail-ures of these methods. 1