Human poses and motions are important cues for analysis of videos with people and there is a strong evidence that representations based on body pose are highly effective for a variety of tasks such as activity recognition, content retrieval and social signal processing. In this workshop, we aim to further advance the state of the art by establishing a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis. The workshop will be organized around a challenge with three competition tracks focusing on single frame multi-person pose estimation, multi-person pose estimation in videos, and multi-person articulated tracking.

To facilitate such benchmark and challenge we collect, annotate and release a new dataset that features videos with multiple people labeled with person tracks, articulated pose and occlusion labels. A centralized evaluation server will be set up to allow participants to evaluate on a held-out test set. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods.