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Action recognition and human pose estimation are closely related but bothproblems are generally handled as distinct tasks in the literature. In thiswork, we propose a multitask framework for jointly 2D and 3D pose estimationfrom still images and human action recognition from video sequences. We showthat a single architecture can be used to solve the two problems in anefficient way and still achieves state-of-the-art results. Additionally, wedemonstrate that optimization from end-to-end leads to significantly higheraccuracy than separated learning. The proposed architecture can be trained withdata from different categories simultaneously in a seamlessly way. The reportedresults on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate theeffectiveness of our method on the targeted tasks.

2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning