Method Overview of D-EventEgo
Qualitative results
Estimating human pose using a front-facing egocentric camera is essential for applications such as sports motion analysis, VR/AR, and AI for wearable devices. However, many existing methods rely on RGB cameras and do not account for low-light environments or motion blur. Event-based cameras have the potential to address these challenges.
In this work, we introduce a novel task of human pose estimation using a front-facing event-based camera mounted on the head and propose D-EventEgo, the first framework for this task. The proposed method first estimates the head poses, and then these are used as conditions to generate body pose. However, when estimating head pose, the presence of dynamic objects mixed with background events may reduce head pose estimation accuracy. Therefore, we introduce Motion Segmentation Module to remove dynamic objects and extract background information.
Extensive experiments on our synthetic eventbased dataset, derived from EgoBody, demonstrate that our approach outperforms our baseline in four out of five evaluation metrics in dynamic environments.
@inproceedings{ikeda2025deventego,
author = {Ikeda, Wataru and Hatano, Masashi and Hara, Ryosei and Isogawa, Mariko},
booktitle = {Event-based Egocentric Human Pose Estimation in Dynamic Environment},
journal = {IEEE International Conference on Image Processing (ICIP)},
year = {2025},
}