EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera [IJCV]

June 13, 2025 · View on GitHub

Christen Millerdurai^1,2, Hiroyasu Akada¹, Jian Wang¹, Diogo Luvizon¹, Alain Pagani², Didier Stricker², Christian Theobalt¹, Vladislav Golyanik¹

¹ Max Planck Institute for Informatics, SIC ² DFKI Augmented Vision

Official PyTorch implementation

Project page | arXiv | IJCV

EventEgo3D

Abstract

Monocular egocentric 3D human motion capture remains a significant challenge, particularly under conditions of low lighting and fast movements, which are common in head-mounted device applications. Existing methods that rely on RGB cameras often fail under these conditions. To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture. Event cameras excel in high-speed scenarios and varying illumination due to their high temporal resolution, providing reliable cues for accurate 3D human motion capture. EventEgo3D++ leverages the LNES representation of event streams to enable precise 3D reconstructions. We have also developed a mobile head-mounted device (HMD) prototype equipped with an event camera, capturing a comprehensive dataset that includes real event observations from both controlled studio environments and in-the-wild settings, in addition to a synthetic dataset. Additionally, to provide a more holistic dataset, we include allocentric RGB streams that offer different perspectives of the HMD wearer, along with their corresponding SMPL body model. Our experiments demonstrate that EventEgo3D++ achieves superior 3D accuracy and robustness compared to existing solutions, even in challenging conditions. Moreover, our method supports real-time 3D pose updates at a rate of 140Hz. This work is an extension of the EventEgo3D approach (CVPR 2024) and further advances the state of the art in egocentric 3D human motion capture

Advantages of Event Based Vision

High Speed Motion	Low Light Performance

Method

EventEgo3D

Usage

EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera

Installation

Clone the repository

git clone https://github.com/Chris10M/EventEgo3D_plus_plus.git
cd EventEgo3D_plus_plus

Dependencies

Create a conda enviroment from the file

conda env create -f EventEgo3D.yml

Next, install ocam_python using pip

pip3 install git+https://github.com/Chris10M/ocam_python.git

Pretrained Models

The pretrained models for EE3D-S, EE3D-R and EE3D-W can be downloaded from

Please place the models in the following folder structure.

EventEgo3D_plus_plus
|
└── saved_models
         |
         └── EE3D-S_pretrained_weights.pth
         └── EE3D_R_finetuned_weights.pth
         └── EE3D_W_finetuned_weights.pth

Datasets

The datasets can obtained by executing the files in dataset_scripts. For detailed information, refer here.

Training

For training, ensure EE3D-S, EE3D-R, EE3D-W and EE3D[BG-AUG] are present. The batch size and checkpoint path can be specified with the following environment variables, BATCH_SIZE and CHECKPOINT_PATH.

python train.py

Evaluation

EE3D-S

For evaluation, ensure EE3D-S Test is present. Please run,

python evaluate_ee3d_s.py

The provided pretrained checkpoint gives us an accuracy of,

Arch	Head_MPJPE	Neck_MPJPE	Right_shoulder_MPJPE	Right_elbow_MPJPE	Right_wrist_MPJPE	Left_shoulder_MPJPE	Left_elbow_MPJPE	Left_wrist_MPJPE	Right_hip_MPJPE	Right_knee_MPJPE	Right_ankle_MPJPE	Right_foot_MPJPE	Left_hip_MPJPE	Left_knee_MPJPE	Left_ankle_MPJPE	Left_foot_MPJPE	MPJPE	Head_PAMPJPE	Neck_PAMPJPE	Right_shoulder_PAMPJPE	Right_elbow_PAMPJPE	Right_wrist_PAMPJPE	Left_shoulder_PAMPJPE	Left_elbow_PAMPJPE	Left_wrist_PAMPJPE	Right_hip_PAMPJPE	Right_knee_PAMPJPE	Right_ankle_PAMPJPE	Right_foot_PAMPJPE	Left_hip_PAMPJPE	Left_knee_PAMPJPE	Left_ankle_PAMPJPE	Left_foot_PAMPJPE	PAMPJPE
EgoHPE	18.794	20.629	34.370	62.688	87.136	36.535	73.797	107.610	73.904	116.881	176.932	191.418	73.927	120.475	186.601	197.100	98.675	35.090	32.134	35.672	61.661	84.088	36.707	59.447	90.251	52.273	75.313	97.924	109.323	51.162	77.778	98.785	104.684	68.893

EE3D-R

For evaluation, ensure EE3D-R is present. Please run,

python evaluate_ee3d_r.py

The provided pretrained checkpoint gives us an accuracy of,

Arch	walk_MPJPE	crouch_MPJPE	pushup_MPJPE	boxing_MPJPE	kick_MPJPE	dance_MPJPE	inter. with env_MPJPE	crawl_MPJPE	sports_MPJPE	jump_MPJPE	MPJPE	walk_PAMPJPE	crouch_PAMPJPE	pushup_PAMPJPE	boxing_PAMPJPE	kick_PAMPJPE	dance_PAMPJPE	inter. with env_PAMPJPE	crawl_PAMPJPE	sports_PAMPJPE	jump_PAMPJPE	PAMPJPE
EgoHPE	68.673	157.415	88.633	123.567	102.313	84.955	95.733	109.378	94.898	95.935	102.150	50.060	100.759	66.288	94.516	84.264	66.906	68.201	75.726	72.233	75.831	75.479

EE3D-W

For evaluation, ensure EE3D-W is present. Please run,

python evaluate_ee3d_w.py

The provided pretrained checkpoint gives us an accuracy of,

Arch	walk_MPJPE	crouch_MPJPE	pushup_MPJPE	boxing_MPJPE	kick_MPJPE	dance_MPJPE	inter. with env_MPJPE	crawl_MPJPE	sports_MPJPE	jump_MPJPE	MPJPE	walk_PAMPJPE	crouch_PAMPJPE	pushup_PAMPJPE	boxing_PAMPJPE	kick_PAMPJPE	dance_PAMPJPE	inter. with env_PAMPJPE	crawl_PAMPJPE	sports_PAMPJPE	jump_PAMPJPE	PAMPJPE
EgoHPE	164.634	160.878	171.486	145.806	172.317	163.608	164.298	151.324	193.632	173.872	166.185	93.441	96.686	105.231	69.619	89.755	97.718	90.325	85.122	104.570	98.185	93.065

Citation

If you find this code useful for your research, please cite our paper:

@article{eventegoplusplus,
author={Millerdurai, Christen
and Akada, Hiroyasu
and Wang, Jian
and Luvizon, Diogo
and Pagani, Alain
and Stricker, Didier
and Theobalt, Christian
and Golyanik, Vladislav},
title={EventEgo3D++: 3D Human Motion Capture from A Head-Mounted Event Camera},
journal={International Journal of Computer Vision (IJCV)},
year={2025},
month={Jun},
day={11},
issn={1573-1405},
doi={10.1007/s11263-025-02489-1},
}

License

EventEgo3D++ is under CC-BY-NC 4.0 license. The license also applies to the pre-trained models.

Acknowledgements

The code is partially adapted from here.