# EgoGen: An Egocentric Synthetic Data Generator

**Authors:** Gen Li, Kaifeng Zhao, Siwei Zhang, Xiaozhong Lyu, Mihai Dusmanu, Yan Zhang, Marc Pollefeys, Siyu Tang  
**arXiv:** [2401.08739](https://arxiv.org/abs/2401.08739)  
**Date:** 2024-01-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.08739v1/

## Key points

- Understanding the world from a first-person perspective is crucial in Augmented Reality (AR)
- Synthetic data has been successful for training vision models for third-person views, but not for egocentric perception tasks
- EgoGen is a synthetic data generator that produces accurate and rich ground-truth training data for egocentric perception tasks
- EgoGen utilizes a groundbreaking human motion synthesis model to perceive the 3D environment
- It incorporates collision-avoiding motion primitives and employs a two-stage reinforcement learning approach
- EgoGen eliminates the need for a pre-defined global path and can be directly applied to dynamic environments
- It is effective in mapping and localization for head-mounted cameras, egocentric camera tracking, and recovering human mesh from egocentric views
- EgoGen will be fully open-sourced and aims to serve as a valuable tool for researchers working on egocentric computer vision research

## Summary

Understanding the world from a first-person perspective is crucial in Augmented Reality (AR), as it presents unique challenges and visual changes compared to third-person views. While synthetic data has been successful in training vision models for third-person views, its application to egocentric perception tasks has been largely unexplored. One of the main challenges in this domain is simulating natural human movements and behaviors that accurately capture the egocentric representation of the 3D world. To address this challenge, the authors introduce EgoGen, a novel synthetic data generator that produces accurate and rich ground-truth training data for egocentric perception tasks. At the core of EgoGen is a groundbreaking human motion synthesis model that utilizes egocentric visual inputs from a virtual human to perceive the 3D environment. This model incorporates collision-avoiding motion primitives and employs a two-stage reinforcement learning approach, resulting in a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly integrated. Unlike previous works, EgoGen eliminates the need for a pre-defined global path and can be directly applied to dynamic environments. The authors also provide an easy-to-use and scalable data generation pipeline, showcasing EgoGen's efficacy in three specific tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and recovering human mesh from egocentric views. EgoGen will be fully open-sourced, making it a practical solution for creating realistic egocentric training data. It aims to serve as a valuable tool for researchers working on egocentric computer vision research. For more information about EgoGen, refer to their project page at https://ego-gen.github.io/.