# Towards Good Practices for Multi-Person Pose Estimation

Source: [Towards Good Practices for Multi-Person Pose Estimation](<https://www.sota2.com/research/paper/68fa66ab1e209131cea118c9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:55:21.281Z

Authors: Dongdong Yu, Kai Su, Changhu Wang

Published: 2019-10-28T03:00:28.000Z

## Abstract

Multi-Person Pose Estimation is an interesting yet challenging task in computer vision. In this paper, we conduct a series of refinements with the MSPN and PoseFix Networks, and empirically evaluate their impact on the final model performance through ablation studies. By taking all the refinements, we achieve 78.7 on the COCO test-dev dataset and 76.3 on the COCO test-challenge dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.07938>)

[PDF](<https://arxiv.org/pdf/1911.07938.pdf>)