1 research outputs found
Pose for Action - Action for Pose
In this work we propose to utilize information about human actions to improve
pose estimation in monocular videos. To this end, we present a pictorial
structure model that exploits high-level information about activities to
incorporate higher-order part dependencies by modeling action specific
appearance models and pose priors. However, instead of using an additional
expensive action recognition framework, the action priors are efficiently
estimated by our pose estimation framework. This is achieved by starting with a
uniform action prior and updating the action prior during pose estimation. We
also show that learning the right amount of appearance sharing among action
classes improves the pose estimation. We demonstrate the effectiveness of the
proposed method on two challenging datasets for pose estimation and action
recognition with over 80,000 test images.Comment: Accepted to FG-201