3 D Human Pose Estimation Using Iterative Conditional Squeeze and Excitation Networks.
We propose a new method for single camera real world 3 D human pose estimation. Our method uses multitask training together with iterative pose refinement using a novel conditional attention mechanism. For iterative pose refinement, the output of each convolutional layer is conditioned on the latest pose estimate, using a conditioned squeeze and excitation network architecture that incorporates novel feedback connections. Multitask training on both an in the wild 2 D pose dataset and a controlled 3 D pose dataset allows for real world 3 D pose estimation without the need for a large scale in the wild 3 D pose dataset, which is unavailable. Experiments are performed on several real world datasets, as well as the Human 3.6 Million and HumanEva I datasets, to show that the combined attention mechanism, iterative refinement scheme, and multitask training allow us to achieve robust and competitive performance with only a simple network architecture. In addition, we show that our method is efficient enough to run on commodity hardware, producing pose estimates in real time.
IEEE transactions on cybernetics