# GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Network

arXiv: https://arxiv.org/abs/2510.07990 · HTML: https://mlsanity.com/paper/2510.07990/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 9 Oct 2025.

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

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera](https://mlsanity.com/paper/1908.11505/) (2019)
- [Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2006.01423/) (2020)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://mlsanity.com/paper/1912.01584/) (2019)
- [EventHPE: Event-based 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2108.06819/) (2021)
- [Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information](https://mlsanity.com/paper/1905.03466/) (2019)
- [3D Human Motion Prediction: A Survey](https://mlsanity.com/paper/2203.01593/) (2022)
- [Peeking into occluded joints: A novel framework for crowd pose estimation](https://mlsanity.com/paper/2003.10506/) (2020)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [High Speed Event Camera TRacking](https://mlsanity.com/paper/2010.02771/) (2020)
- [Lifting Monocular Events to 3D Human Poses](https://mlsanity.com/paper/2104.10609/) (2021)
- [Powerline Tracking with Event Cameras](https://mlsanity.com/paper/2108.00515/) (2021)
- [Survey of Image Based Graph Neural Networks](https://mlsanity.com/paper/2106.06307/) (2021)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [Single Person Pose Estimation: A Survey](https://mlsanity.com/paper/2109.10056/) (2021)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)

## Built on by (most cited first, 1 of 1)

- [FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision](https://mlsanity.com/paper/2603.19770/) (2026)

## Abstract (arXiv)

Human Pose Estimation is a crucial module in human-machine interaction applications and, especially since the rise in deep learning technology, robust methods are available to consumers using RGB cameras and commercial GPUs. On the other hand, event-based cameras have gained popularity in the vision research community for their low latency and low energy advantages that make them ideal for applications where those resources are constrained like portable electronics and mobile robots. In this work we propose a Graph Neural Network, GraphEnet, that leverages the sparse nature of event camera output, with an intermediate line based event representation, to estimate 2D Human Pose of a single person at a high frequency. The architecture incorporates a novel offset vector learning paradigm with confidence based pooling to estimate the human pose. This is the first work that applies Graph Neural Networks to event data for Human Pose Estimation. The code is open-source at https://github.com/event-driven-robotics/GraphEnet-NeVi-ICCV2025.