TransPose: Keypoint Localization via Transformer

Understand

While CNN-based models have made remarkable progress on human pose estimation, what spatial dependencies they capture to localize keypoints remains unclear.

• In this work, we propose a model called \textbf{TransPose}, which introduces Transformer for human pose estimation.
• The attention layers built in Transformer enable our model to capture long-range relationships efficiently and also can reveal what dependencies the predicted keypoints rely on.
• To predict keypoint heatmaps, the last attention layer acts as an aggregator, which collects contributions from image clues and forms maximum positions of keypoints.