# A survey of top-down approaches for human pose estimation

arXiv: https://arxiv.org/abs/2202.02656 · HTML: https://mlsanity.com/paper/2202.02656/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 80 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (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)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Towards Accurate Multi-person Pose Estimation in the Wild](https://mlsanity.com/paper/1701.01779/) (2017)
- [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation](https://mlsanity.com/paper/2103.12605/) (2021)
- [Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2006.01423/) (2020)
- [Combining detection and tracking for human pose estimation in videos](https://mlsanity.com/paper/2003.13743/) (2020)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS](https://mlsanity.com/paper/2003.03972/) (2020)
- [Mixture Dense Regression for Object Detection and Human Pose Estimation](https://mlsanity.com/paper/1912.00821/) (2019)
- [Unsupervised Human Pose Estimation through Transforming Shape Templates](https://mlsanity.com/paper/2105.04154/) (2021)
- [Globally Optimal Relative Pose Estimation with Gravity Prior](https://mlsanity.com/paper/2012.00458/) (2020)
- [Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking](https://mlsanity.com/paper/2106.11589/) (2021)
- [PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds](https://mlsanity.com/paper/1912.09057/) (2019)
- [Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking](https://mlsanity.com/paper/2106.08045/) (2021)

## Built on by (most cited first, 3 of 3)

- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images](https://mlsanity.com/paper/2506.19747/) (2025)
- [A Topology-Aware Graph Convolutional Network for Human Pose Similarity and Action Quality Assessment](https://mlsanity.com/paper/2511.01194/) (2025)

## Abstract (arXiv)

Human pose estimation in two-dimensional images videos has been a hot topic in the computer vision problem recently due to its vast benefits and potential applications for improving human life, such as behaviors recognition, motion capture and augmented reality, training robots, and movement tracking. Many state-of-the-art methods implemented with Deep Learning have addressed several challenges and brought tremendous remarkable results in the field of human pose estimation. Approaches are classified into two kinds: the two-step framework (top-down approach) and the part-based framework (bottom-up approach). While the two-step framework first incorporates a person detector and then estimates the pose within each box independently, detecting all body parts in the image and associating parts belonging to distinct persons is conducted in the part-based framework. This paper aims to provide newcomers with an extensive review of deep learning methods-based 2D images for recognizing the pose of people, which only focuses on top-down approaches since 2016. The discussion through this paper presents significant detectors and estimators depending on mathematical background, the challenges and limitations, benchmark datasets, evaluation metrics, and comparison between methods.