# Multi-Person 3D Human Pose Estimation from Monocular Images

arXiv: https://arxiv.org/abs/1909.10854 · HTML: https://mlsanity.com/paper/1909.10854/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [Reconstructing 3D Human Pose by Watching Humans in the Mirror](https://mlsanity.com/paper/2104.00340/) (2021)
- [Gravity-Aware Monocular 3D Human-Object Reconstruction](https://mlsanity.com/paper/2108.08844/) (2021)
- [Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization](https://mlsanity.com/paper/2203.11471/) (2022)
- [Scene-Aware 3D Multi-Human Motion Capture from a Single Camera](https://mlsanity.com/paper/2301.05175/) (2023)
- [MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes](https://mlsanity.com/paper/2208.08439/) (2022)
- [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)
- [Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting](https://mlsanity.com/paper/2104.03520/) (2021)
- [Best Practices for 2-Body Pose Forecasting](https://mlsanity.com/paper/2304.05758/) (2023)
- [NIMBLE: A Non-rigid Hand Model with Bones and Muscles](https://mlsanity.com/paper/2202.04533/) (2022)
- [Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge](https://mlsanity.com/paper/2211.11983/) (2022)
- [Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2204.04913/) (2022)
- [3D Keypoint Estimation Using Implicit Representation Learning](https://mlsanity.com/paper/2306.11529/) (2023)
- [Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV](https://mlsanity.com/paper/2203.02453/) (2022)
- [Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection](https://mlsanity.com/paper/2206.03061/) (2022)
- [Szloca: towards a framework for full 3D tracking through a single camera in context of interactive arts](https://mlsanity.com/paper/2206.12958/) (2022)
- [Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation](https://mlsanity.com/paper/2207.07900/) (2022)

## Abstract (arXiv)

Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose HG-RCNN, a Mask-RCNN based network that also leverages the benefits of the Hourglass architecture for multi-person 3D Human Pose Estimation. A two-staged approach is presented that first estimates the 2D keypoints in every Region of Interest (RoI) and then lifts the estimated keypoints to 3D. Finally, the estimated 3D poses are placed in camera-coordinates using weak-perspective projection assumption and joint optimization of focal length and root translations. The result is a simple and modular network for multi-person 3D human pose estimation that does not require any multi-person 3D pose dataset. Despite its simple formulation, HG-RCNN achieves the state-of-the-art results on MuPoTS-3D while also approximating the 3D pose in the camera-coordinate system.