DirectPose: Direct End-to-End Multi-Person Pose Estimation
Zhi Tian, Hao Chen, Chunhua Shen

TL;DR
DirectPose introduces a novel end-to-end framework for multi-person pose estimation that directly predicts keypoints without heuristic grouping or bounding-box detection, significantly simplifying the process.
Contribution
It is the first to propose a fully end-to-end multi-person pose estimation framework with a novel Keypoint Alignment mechanism, eliminating the need for traditional heuristic postprocessing.
Findings
Achieves competitive or superior performance compared to existing methods.
Simplifies multi-person pose estimation by removing heuristic grouping.
Maintains end-to-end trainability with a single postprocessing step.
Abstract
We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose. Inspired by recent anchor-free object detectors, which directly regress the two corners of target bounding-boxes, the proposed framework directly predicts instance-aware keypoints for all the instances from a raw input image, eliminating the need for heuristic grouping in bottom-up methods or bounding-box detection and RoI operations in top-down ones. We also propose a novel Keypoint Alignment (KPAlign) mechanism, which overcomes the main difficulty: lack of the alignment between the convolutional features and predictions in this end-to-end framework. KPAlign improves the framework's performance by a large margin while still keeping the framework end-to-end trainable. With the only postprocessing non-maximum suppression (NMS), our proposed framework can detect multi-person keypoints with or…
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Taxonomy
TopicsHuman Pose and Action Recognition · Video Surveillance and Tracking Methods · Anomaly Detection Techniques and Applications
MethodsAverage Pooling · Residual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · 1x1 Convolution · Batch Normalization · Bottleneck Residual Block · Global Average Pooling · Residual Block · Kaiming Initialization · Max Pooling
