# Context Modeling in 3D Human Pose Estimation: A Unified Perspective

arXiv: https://arxiv.org/abs/2103.15507 · HTML: https://mlsanity.com/paper/2103.15507/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [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)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [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)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019)
- [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](https://mlsanity.com/paper/1406.2984/) (2014)
- [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Ordinal Depth Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/1805.04095/) (2018)
- [Cross View Fusion for 3D Human Pose Estimation](https://mlsanity.com/paper/1909.01203/) (2019)
- [3D Human Pose Estimation in the Wild by Adversarial Learning](https://mlsanity.com/paper/1803.09722/) (2018)
- [Harvesting Multiple Views for Marker-less 3D Human Pose Annotations](https://mlsanity.com/paper/1704.04793/) (2017)
- [OriNet: A Fully Convolutional Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1811.04989/) (2018)
- [Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations](https://mlsanity.com/paper/1407.3399/) (2014)
- [Robust Estimation of 3D Human Poses from a Single Image](https://mlsanity.com/paper/1406.2282/) (2014)
- [Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts](https://mlsanity.com/paper/1905.07862/) (2019)
- [3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach](https://mlsanity.com/paper/1411.2942/) (2014)

## Built on by (most cited first, 30 of 37)

- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [DiffPose: Toward More Reliable 3D Pose Estimation](https://mlsanity.com/paper/2211.16940/) (2022)
- [GFPose: Learning 3D Human Pose Prior with Gradient Fields](https://mlsanity.com/paper/2212.08641/) (2022)
- [GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video](https://mlsanity.com/paper/2307.05853/) (2023)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2212.07729/) (2022)
- [Proactive Multi-Camera Collaboration For 3D Human Pose Estimation](https://mlsanity.com/paper/2303.03767/) (2023)
- [A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation](https://mlsanity.com/paper/2311.03312/) (2023)
- [VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data](https://mlsanity.com/paper/2207.09949/) (2022)
- [Dynamic Graph Reasoning for Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2207.11341/) (2022)
- [LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network](https://mlsanity.com/paper/2306.12525/) (2023)
- [MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation](https://mlsanity.com/paper/2509.00649/) (2025)
- [A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion](https://mlsanity.com/paper/2207.07381/) (2022)
- [3D Human Pose Estimation Using M"obius Graph Convolutional Networks](https://mlsanity.com/paper/2203.10554/) (2022)
- [CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators](https://mlsanity.com/paper/2407.06141/) (2024)
- [GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction](https://mlsanity.com/paper/2411.06725/) (2024)
- [Occlusion Handling in 3D Human Pose Estimation with Perturbed Positional Encoding](https://mlsanity.com/paper/2405.17397/) (2024)
- [PersPose: 3D Human Pose Estimation with Perspective Encoding and Perspective Rotation](https://mlsanity.com/paper/2508.17239/) (2025)
- [Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation](https://mlsanity.com/paper/2402.02339/) (2024)
- [An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video](https://mlsanity.com/paper/2404.06741/) (2024)
- [Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training](https://mlsanity.com/paper/2404.19279/) (2024)
- [FaceLift: Semi-supervised 3D Facial Landmark Localization](https://mlsanity.com/paper/2405.19646/) (2024)
- [CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty](https://mlsanity.com/paper/2412.10431/) (2024)
- [Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2505.20611/) (2025)
- [ActionMap: Robot Policy Learning via Voxel Action Heatmap](https://mlsanity.com/paper/2606.06904/) (2026)
- [Decanus to Legatus: Synthetic training for 2D-3D human pose lifting](https://mlsanity.com/paper/2210.02231/) (2022)
- [Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning](https://mlsanity.com/paper/2311.02815/) (2023)
- [VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data](https://mlsanity.com/paper/2312.08871/) (2023)

## Abstract (arXiv)

Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance.