# ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation

arXiv: https://arxiv.org/abs/2202.00753 · HTML: https://mlsanity.com/paper/2202.00753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Pose2Seg: Detection Free Human Instance Segmentation](https://mlsanity.com/paper/1803.10683/) (2018)
- [PANDA: A Gigapixel-level Human-centric Video Dataset](https://mlsanity.com/paper/2003.04852/) (2020)
- [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://mlsanity.com/paper/1811.12004/) (2018)
- [You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery](https://mlsanity.com/paper/1805.09512/) (2018)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)
- [EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation](https://mlsanity.com/paper/2007.08090/) (2020)
- [Satellite Imagery Multiscale Rapid Detection with Windowed Networks](https://mlsanity.com/paper/1809.09978/) (2018)
- [Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects](https://mlsanity.com/paper/1811.12152/) (2018)
- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)

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

- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [CHAD: Charlotte Anomaly Dataset](https://mlsanity.com/paper/2212.09258/) (2022)

## Abstract (arXiv)

Recent advancements in computer vision have seen a rise in the prominence of applications using neural networks to understand human poses. However, while accuracy has been steadily increasing on State-of-the-Art datasets, these datasets often do not address the challenges seen in real-world applications. These challenges are dealing with people distant from the camera, people in crowds, and heavily occluded people. As a result, many real-world applications have trained on data that does not reflect the data present in deployment, leading to significant underperformance. This article presents ADG-Pose, a method for automatically generating datasets for real-world human pose estimation. These datasets can be customized to determine person distances, crowdedness, and occlusion distributions. Models trained with our method are able to perform in the presence of these challenges where those trained on other datasets fail. Using ADG-Pose, end-to-end accuracy for real-world skeleton-based action recognition sees a 20% increase on scenes with moderate distance and occlusion levels, and a 4X increase on distant scenes where other models failed to perform better than random.