SD-Pose: facilitating space-decoupled human pose estimation via adaptive pose perception guidance

Human pose estimation is a popular and challenging task in computer vision. Currently, the mainstream methods for pose estimation are based on Gaussian heatmaps and coordinate regression techniques. However, the intensive computational overhead and quantization error introduced by heatmaps pose many limitations on their application. And coordinate regression faces difficulties in learning mapping cross and misaligned keypoints, resulting in poor robustness. Recently, pose estimation based on Coordinate Classification encodes global spatial information into one-dimensional representations in X and Y directions, which turns keypoint localization into a classification problem and thus simplifies the model while effectively improving pose estimation accuracy. Motivated by this, SD-Pose is proposed in this work, which is a spatially decoupled human pose estimation model guided by adaptive pose perception. Specifically, the model first employs a Pyramid Adaptive Feature Extractor (PAFE) to obtain multi-scale featuremaps and generate adaptive keypoint weights to assist the model in extracting unique features for keypoints at different locations. Then, the Spatial Decoupling and Coordinated Analysis Module (SDCAM) simplifies the localization problem while considering both global and fine-grained features. Experimental results on MPII human pose and COCO keypoint detection datasets validate the effectiveness of the SD-Pose model and also display satisfied performance in recovering detailed information for keypoints such as Elbow, Hip, and Ankle.