# EventHands: Real-Time Neural 3D Hand Pose Estimation from an Event Stream

arXiv: https://arxiv.org/abs/2012.06475 · HTML: https://mlsanity.com/paper/2012.06475/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 30 of 48)

- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Event Transformer. A sparse-aware solution for efficient event data processing](https://mlsanity.com/paper/2204.03355/) (2022)
- [Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion](https://mlsanity.com/paper/2309.08596/) (2023)
- [Deformable Neural Radiance Fields using RGB and Event Cameras](https://mlsanity.com/paper/2309.08416/) (2023)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams](https://mlsanity.com/paper/2404.08640/) (2024)
- [HARP: Personalized Hand Reconstruction from a Monocular RGB Video](https://mlsanity.com/paper/2212.09530/) (2022)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [E-MLB: Multilevel Benchmark for Event-Based Camera Denoising](https://mlsanity.com/paper/2303.11997/) (2023)
- [High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition](https://mlsanity.com/paper/2307.05541/) (2023)
- [Event-based Non-Rigid Reconstruction from Contours](https://mlsanity.com/paper/2210.06270/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera](https://mlsanity.com/paper/2312.14157/) (2023)
- [THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision](https://mlsanity.com/paper/2210.13853/) (2022)
- [Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction](https://mlsanity.com/paper/2403.07346/) (2024)
- [Event Transformer+. A multi-purpose solution for efficient event data processing](https://mlsanity.com/paper/2211.12222/) (2022)
- [SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing](https://mlsanity.com/paper/2311.05310/) (2023)
- [HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks](https://mlsanity.com/paper/2107.01205/) (2021)
- [Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer](https://mlsanity.com/paper/2303.09681/) (2023)
- [HOIST-Former: Hand-held Objects Identification, Segmentation, and Tracking in the Wild](https://mlsanity.com/paper/2404.13819/) (2024)
- [Helios: An extremely low power event-based gesture recognition for always-on smart eyewear](https://mlsanity.com/paper/2407.05206/) (2024)
- [EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera](https://mlsanity.com/paper/2502.07869/) (2025)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [EventEgoHands: Event-based Egocentric 3D Hand Mesh Reconstruction](https://mlsanity.com/paper/2505.19169/) (2025)
- [Continuous Pose for Monocular Cameras in Neural Implicit Representation](https://mlsanity.com/paper/2311.17119/) (2023)
- [Dynamic EventNeRF: Reconstructing General Dynamic Scenes from Multi-view RGB and Event Streams](https://mlsanity.com/paper/2412.06770/) (2024)
- [EgoEvGesture: Gesture Recognition Based on Egocentric Event Camera](https://mlsanity.com/paper/2503.12419/) (2025)

## Abstract (arXiv)

3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor reacting on brightness changes. Our EventHands approach has characteristics previously not demonstrated with a single RGB or depth camera such as high temporal resolution at low data throughputs and real-time performance at 1000 Hz. Due to the different data modality of event cameras compared to classical cameras, existing methods cannot be directly applied to and re-trained for event streams. We thus design a new neural approach which accepts a new event stream representation suitable for learning, which is trained on newly-generated synthetic event streams and can generalise to real data. Experiments show that EventHands outperforms recent monocular methods using a colour (or depth) camera in terms of accuracy and its ability to capture hand motions of unprecedented speed. Our method, the event stream simulator and the dataset are publicly available; see https://4dqv.mpi-inf.mpg.de/EventHands/