# PCIE_EgoHandPose Solution for EgoExo4D Hand Pose Challenge

arXiv: https://arxiv.org/abs/2406.12219 · HTML: https://mlsanity.com/paper/2406.12219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (2021)
- [POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery](https://mlsanity.com/paper/2303.13357/) (2023)

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

- [EgoHandICL: Egocentric 3D Hand Reconstruction with In-Context Learning](https://mlsanity.com/paper/2601.19850/) (2026)
- [EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera](https://mlsanity.com/paper/2605.12498/) (2026)
- [PCIE_Pose Solution for EgoExo4D Pose and Proficiency Estimation Challenge](https://mlsanity.com/paper/2505.24411/) (2025)

## Abstract (arXiv)

This report presents our team's 'PCIE_EgoHandPose' solution for the EgoExo4D Hand Pose Challenge at CVPR2024. The main goal of the challenge is to accurately estimate hand poses, which involve 21 3D joints, using an RGB egocentric video image provided for the task. This task is particularly challenging due to the subtle movements and occlusions. To handle the complexity of the task, we propose the Hand Pose Vision Transformer (HP-ViT). The HP-ViT comprises a ViT backbone and transformer head to estimate joint positions in 3D, utilizing MPJPE and RLE loss function. Our approach achieved the 1st position in the Hand Pose challenge with 25.51 MPJPE and 8.49 PA-MPJPE. Code is available at https://github.com/KanokphanL/PCIE_EgoHandPose