Integral Human Pose Regression

Abstract

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.

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Cited by in corpus (8)

• TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
• TFPose: Direct Human Pose Estimation with Transformers
• How Robust is 3D Human Pose Estimation to Occlusion?
• Towards Robust RGB-D Human Mesh Recovery
• An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
• Patch-based 3D Human Pose Refinement
• Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning
• Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization