Single-Stage Multi-Person Pose Machines
Xuecheng Nie, Jianfeng Zhang, Shuicheng Yan, Jiashi Feng

TL;DR
This paper introduces a novel single-stage model, SPM, for multi-person pose estimation that unifies person and joint representations, significantly improving efficiency while maintaining high accuracy.
Contribution
The paper proposes the first single-stage multi-person pose estimation model using Structured Pose Representation, simplifying the pipeline and enhancing efficiency over traditional two-stage methods.
Findings
Achieves state-of-the-art efficiency on multiple benchmarks.
Demonstrates high accuracy in 2D and 3D multi-person pose estimation.
Validates the generality of SPM across different datasets.
Abstract
Multi-person pose estimation is a challenging problem. Existing methods are mostly two-stage based--one stage for proposal generation and the other for allocating poses to corresponding persons. However, such two-stage methods generally suffer low efficiency. In this work, we present the first single-stage model, Single-stage multi-person Pose Machine (SPM), to simplify the pipeline and lift the efficiency for multi-person pose estimation. To achieve this, we propose a novel Structured Pose Representation (SPR) that unifies person instance and body joint position representations. Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods. In particular, SPR introduces the root joints to indicate different person instances and…
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Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Anomaly Detection Techniques and Applications
