  Transformer architectures have become the model of choice in natural language
processing and are now being introduced into computer vision tasks such as
image classification, object detection, and semantic segmentation. However, in
the field of human pose estimation, convolutional architectures still remain
dominant. In this work, we present PoseFormer, a purely transformer-based
approach for 3D human pose estimation in videos without convolutional
architectures involved. Inspired by recent developments in vision transformers,
we design a spatial-temporal transformer structure to comprehensively model the
human joint relations within each frame as well as the temporal correlations
across frames, then output an accurate 3D human pose of the center frame. We
quantitatively and qualitatively evaluate our method on two popular and
standard benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments
show that PoseFormer achieves state-of-the-art performance on both datasets.
Code is available at \url{https://github.com/zczcwh/PoseFormer}

Computer Vision and Pattern Recognition;Artificial Intelligence;Human-Computer Interaction