# Real-time Pose Estimation from Images for Multiple Humanoid Robots

arXiv: https://arxiv.org/abs/2107.02675 · HTML: https://mlsanity.com/paper/2107.02675/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 21 of 21)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [Towards Accurate Multi-person Pose Estimation in the Wild](https://mlsanity.com/paper/1701.01779/) (2017)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation](https://mlsanity.com/paper/1707.05388/) (2017)
- [Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation](https://mlsanity.com/paper/1911.10529/) (2019)
- [Online Visual Robot Tracking and Identification using Deep LSTM Networks](https://mlsanity.com/paper/1810.04941/) (2018)
- [NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot](https://mlsanity.com/paper/1810.08395/) (2018)
- [Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots](https://mlsanity.com/paper/1810.06411/) (2018)
- [NimbRo Robots Winning RoboCup 2018 Humanoid AdultSize Soccer Competitions](https://mlsanity.com/paper/1909.02385/) (2019)
- [RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities](https://mlsanity.com/paper/1912.07405/) (2019)

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

- [External Camera-based Mobile Robot Pose Estimation for Collaborative Perception with Smart Edge Sensors](https://mlsanity.com/paper/2303.03797/) (2023)
- [Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots](https://mlsanity.com/paper/2603.06181/) (2026)
- [Augmenting Vision-Based Human Pose Estimation with Rotation Matrix](https://mlsanity.com/paper/2310.06068/) (2023)

## Abstract (arXiv)

Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos. With recent advancements in deep learning, we now have compelling models to tackle the problem in real-time. Since these models are usually designed for human images, one needs to adapt existing models to work on other creatures, including robots. This paper examines different state-of-the-art pose estimation models and proposes a lightweight model that can work in real-time on humanoid robots in the RoboCup Humanoid League environment. Additionally, we present a novel dataset called the HumanoidRobotPose dataset. The results of this work have the potential to enable many advanced behaviors for soccer-playing robots.