Human pose estimation has achieved tremendous advances in accuracy with the emergence of various deep neural network architectures. However, for low-resolution (LR) images, we are far from achieving an acceptable accuracy. Deep learning based super-resolution (SR) has been proven to be helpful for addressing the challenges of face recognition and object detection in LR images. Following this idea, we integrate SR into existing human pose estimation networks to increase accuracy for LR images. In this work, we propose a novel end-to-end network architecture for the effective combination of SR and human pose estimation. Moreover, an approach is presented to guide the SR network to generate intermediate high-resolution (HR) images that contribute to pose estimation, rather than simply taking SR as an upstream task. The experimental results show that the accuracy of our approach has over 20% improved to that of the interpolation-assisted pose estimation network on the downsampled MPII dataset.

随着各种深度神经网络体系结构的出现，人体姿势估计在准确性方面取得了巨大进步。但是，对于低分辨率（LR）图像，我们离实现可接受的精度还差得很远。事实证明，基于深度学习的超分辨率（SR）有助于解决LR图像中的面部识别和对象检测挑战。遵循这个想法，我们将SR集成到现有的人体姿势估计网络中，以提高LR图像的准确性。在这项工作中，我们提出了一种新颖的端到端网络体系结构，用于SR和人体姿态估计的有效结合。此外，提出了一种方法来指导SR网络生成有助于姿态估计的中间高分辨率（HR）图像，而不是简单地将SR作为上游任务。