# Leveraging RGB Images for Pre-Training of Event-Based Hand Pose Estimation

arXiv: https://arxiv.org/abs/2509.16949 · HTML: https://mlsanity.com/paper/2509.16949/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.09309/) (2020)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [ESS: Learning Event-based Semantic Segmentation from Still Images](https://mlsanity.com/paper/2203.10016/) (2022)
- [EventHands: Real-Time Neural 3D Hand Pose Estimation from an Event Stream](https://mlsanity.com/paper/2012.06475/) (2020)
- [Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.02618/) (2021)
- [Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects](https://mlsanity.com/paper/2403.16428/) (2024)
- [EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition](https://mlsanity.com/paper/2403.14082/) (2024)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction](https://mlsanity.com/paper/2403.07346/) (2024)

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

- [DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactions](https://mlsanity.com/paper/2512.02727/) (2025)
- [EventFace: Event-Based Face Recognition via Structure-Driven Spatiotemporal Modeling](https://mlsanity.com/paper/2604.06782/) (2026)
- [EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset](https://mlsanity.com/paper/2609.17189/) (2026)

## Abstract (arXiv)

This paper presents RPEP, the first pre-training method for event-based 3D hand pose estimation using labeled RGB images and unpaired, unlabeled event data. Event data offer significant benefits such as high temporal resolution and low latency, but their application to hand pose estimation is still limited by the scarcity of labeled training data. To address this, we repurpose real RGB datasets to train event-based estimators. This is done by constructing pseudo-event-RGB pairs, where event data is generated and aligned with the ground-truth poses of RGB images. Unfortunately, existing pseudo-event generation techniques assume stationary objects, thus struggling to handle non-stationary, dynamically moving hands. To overcome this, RPEP introduces a novel generation strategy that decomposes hand movements into smaller, step-by-step motions. This decomposition allows our method to capture temporal changes in articulation, constructing more realistic event data for a moving hand. Additionally, RPEP imposes a motion reversal constraint, regularizing event generation using reversed motion. Extensive experiments show that our pre-trained model significantly outperforms state-of-the-art methods on real event data, achieving up to 24% improvement on EvRealHands. Moreover, it delivers strong performance with minimal labeled samples for fine-tuning, making it well-suited for practical deployment.