# Vision-Based Human Pose Estimation via Deep Learning: A Survey

arXiv: https://arxiv.org/abs/2308.13872 · HTML: https://mlsanity.com/paper/2308.13872/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 98 of its 155 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 30 of 98)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [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)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)

## Built on by (most cited first, 20 of 20)

- [Markerless Multi-view 3D Human Pose Estimation: a survey](https://mlsanity.com/paper/2407.03817/) (2024)
- [Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection](https://mlsanity.com/paper/2506.18368/) (2025)
- [Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models](https://mlsanity.com/paper/2506.15290/) (2025)
- [DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones](https://mlsanity.com/paper/2407.13777/) (2024)
- [Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario](https://mlsanity.com/paper/2311.08410/) (2023)
- [Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps](https://mlsanity.com/paper/2509.19252/) (2025)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (2023)
- [Automatic parking planning control method based on improved A* algorithm](https://mlsanity.com/paper/2406.15429/) (2024)
- [ML-SceGen: A Multi-level Scenario Generation Framework](https://mlsanity.com/paper/2501.10782/) (2025)
- [Real-time pedestrian recognition on low computational resources](https://mlsanity.com/paper/2309.01353/) (2023)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Deep Reinforcement Learning for Adverse Garage Scenario Generation](https://mlsanity.com/paper/2407.01333/) (2024)
- [Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots](https://mlsanity.com/paper/2407.02197/) (2024)
- [Development of Occupancy Prediction Algorithm for Underground Parking Lots](https://mlsanity.com/paper/2409.00923/) (2024)
- [Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism](https://mlsanity.com/paper/2503.04863/) (2025)
- [Artificial Behavior Intelligence: Technology, Challenges, and Future Directions](https://mlsanity.com/paper/2505.03315/) (2025)
- [Data Shift of Object Detection in Autonomous Driving](https://mlsanity.com/paper/2508.11868/) (2025)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)
- [Multi-Person Pose Estimation Evaluation Using Optimal Transportation and Improved Pose Matching](https://mlsanity.com/paper/2603.10398/) (2026)
- [NCSTR: Node-Centric Decoupled Spatio-Temporal Reasoning for Video-based Human Pose Estimation](https://mlsanity.com/paper/2603.20323/) (2026)

## Abstract (arXiv)

Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human-computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in the literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.