# PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation

arXiv: https://arxiv.org/abs/2010.05302 · HTML: https://mlsanity.com/paper/2010.05302/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Body Meshes as Points](https://mlsanity.com/paper/2105.02467/) (2021)
- [3D Vision with Transformers: A Survey](https://mlsanity.com/paper/2208.04309/) (2022)
- [HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE](https://mlsanity.com/paper/2204.01565/) (2022)
- [Reconstructing Three-Dimensional Models of Interacting Humans](https://mlsanity.com/paper/2308.01854/) (2023)
- [Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images](https://mlsanity.com/paper/2111.01884/) (2021)
- [Dyadic Human Motion Prediction](https://mlsanity.com/paper/2112.00396/) (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)
- [Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2204.04913/) (2022)
- [Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy](https://mlsanity.com/paper/2306.06117/) (2023)
- [Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation](https://mlsanity.com/paper/2207.07900/) (2022)
- [UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction](https://mlsanity.com/paper/2411.04151/) (2024)
- [Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation](https://mlsanity.com/paper/2505.12130/) (2025)
- [Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting](https://mlsanity.com/paper/2604.00927/) (2026)

## Abstract (arXiv)

Recent literature addressed the monocular 3D pose estimation task very satisfactorily. In these studies, different persons are usually treated as independent pose instances to estimate. However, in many every-day situations, people are interacting, and the pose of an individual depends on the pose of his/her interactees. In this paper, we investigate how to exploit this dependency to enhance current - and possibly future - deep networks for 3D monocular pose estimation. Our pose interacting network, or PI-Net, inputs the initial pose estimates of a variable number of interactees into a recurrent architecture used to refine the pose of the person-of-interest. Evaluating such a method is challenging due to the limited availability of public annotated multi-person 3D human pose datasets. We demonstrate the effectiveness of our method in the MuPoTS dataset, setting the new state-of-the-art on it. Qualitative results on other multi-person datasets (for which 3D pose ground-truth is not available) showcase the proposed PI-Net. PI-Net is implemented in PyTorch and the code will be made available upon acceptance of the paper.