# Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose

Source: [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](<https://www.sota2.com/research/paper/68fa651f1e209131cea08899>)

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

Data updated: 2025-10-25T21:19:52.933Z

Authors: Daniil Osokin

Published: 2018-11-29T08:05:05.000Z

## Abstract

In this work we adapt multi-person pose estimation architecture to use it on edge devices. We follow the bottom-up approach from OpenPose, the winner of COCO 2016 Keypoints Challenge, because of its decent quality and robustness to number of people inside the frame. With proposed network design and optimized post-processing code the full solution runs at 28 frames per second (fps) on Intel$\\unicode{xAE}$ NUC 6i7KYB mini PC and 26 fps on Core$^{TM}$ i7-6850K CPU. The network model has 4.1M parameters and 9 billions floating-point operations (GFLOPs) complexity, which is just ~15% of the baseline 2-stage OpenPose with almost the same quality. The code and model are available as a part of Intel$\\unicode{xAE}$ OpenVINO$^{TM}$ Toolkit.

## 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.

- [2D Human Pose Estimation on COCO 2017 (val)](<https://www.sota2.com/research/sota/2d-human-pose-estimation-on-coco-2017-val.md>): 2D Human Pose Estimation; COCO 2017 (val); AP: 42.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.12004>)

[PDF](<https://arxiv.org/pdf/1811.12004.pdf>)