AiPE: A Novel Transformer-Based Pose Estimation Method

Abstract

Human pose estimation is an important problem in computer vision because it is the foundation for many advanced semantic tasks and downstream applications. Although some convolutional neural network-based pose estimation methods have achieved good results, these networks are still limited for restricted receptive fields and weak robustness, leading to poor detection performance in scenarios with blur or low resolution. Additionally, their highly parallelized strategy is likely to cause significant computational demands, requiring high computing power. In comparison to the convolutional neural networks, the transformer-based methods offer advantages such as flexible stacking, global perspective, and parallel computation. Based on the great benefits, a novel transformer-based human pose estimation method is developed, which employees multi-head self-attention mechanisms and offset windows to effectively suppress the quick growth of the computational complexity near human keypoints. Experimental results under detailed visual comparison and quantitative analysis demonstrate that the proposed method can efficiently deal with the pose estimation problem in challenging scenarios, such as blurry or occluded scenes. Furthermore, the errors in human skeleton mapping caused by keypoint occlusion or omission can be effectively corrected, so the accuracy of pose estimation results is greatly improved.