# 1st Place Solution of Multiview Egocentric Hand Tracking Challenge ECCV2024

arXiv: https://arxiv.org/abs/2409.19362 · HTML: https://mlsanity.com/paper/2409.19362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation](https://mlsanity.com/paper/2304.12301/) (2023)
- [Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation](https://mlsanity.com/paper/2004.02186/) (2020)
- [UmeTrack: Unified multi-view end-to-end hand tracking for VR](https://mlsanity.com/paper/2211.00099/) (2022)
- [Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking](https://mlsanity.com/paper/2406.09598/) (2024)
- [Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects](https://mlsanity.com/paper/2403.16428/) (2024)
- [A Simple Baseline for Efficient Hand Mesh Reconstruction](https://mlsanity.com/paper/2403.01813/) (2024)
- [1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction](https://mlsanity.com/paper/2310.04769/) (2023)

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

- [ESTHER: Egocentric Stereo Hand Estimation and Reconstruction in the Wild](https://mlsanity.com/paper/2609.34817/) (2026)

## Abstract (arXiv)

Multi-view egocentric hand tracking is a challenging task and plays a critical role in VR interaction. In this report, we present a method that uses multi-view input images and camera extrinsic parameters to estimate both hand shape and pose. To reduce overfitting to the camera layout, we apply crop jittering and extrinsic parameter noise augmentation. Additionally, we propose an offline neural smoothing post-processing method to further improve the accuracy of hand position and pose. Our method achieves 13.92mm MPJPE on the Umetrack dataset and 21.66mm MPJPE on the HOT3D dataset.