# An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge

arXiv: https://arxiv.org/abs/1809.06079 · HTML: https://mlsanity.com/paper/1809.06079/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [RUMPL: Ray-Based Transformers for Universal Multi-View 2D to 3D Human Pose Lifting](https://mlsanity.com/paper/2512.15488/) (2025)
- [2D Pre-Training for 3D Pose Estimation](https://mlsanity.com/paper/2604.22830/) (2026)

## Abstract (arXiv)

For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work.