  Understanding the world in first-person view is fundamental in Augmented
Reality (AR). This immersive perspective brings dramatic visual changes and
unique challenges compared to third-person views. Synthetic data has empowered
third-person-view vision models, but its application to embodied egocentric
perception tasks remains largely unexplored. A critical challenge lies in
simulating natural human movements and behaviors that effectively steer the
embodied cameras to capture a faithful egocentric representation of the 3D
world. To address this challenge, we introduce EgoGen, a new synthetic data
generator that can produce accurate and rich ground-truth training data for
egocentric perception tasks. At the heart of EgoGen is a novel human motion
synthesis model that directly leverages egocentric visual inputs of a virtual
human to sense the 3D environment. Combined with collision-avoiding motion
primitives and a two-stage reinforcement learning approach, our motion
synthesis model offers a closed-loop solution where the embodied perception and
movement of the virtual human are seamlessly coupled. Compared to previous
works, our model eliminates the need for a pre-defined global path, and is
directly applicable to dynamic environments. Combined with our easy-to-use and
scalable data generation pipeline, we demonstrate EgoGen's efficacy in three
tasks: mapping and localization for head-mounted cameras, egocentric camera
tracking, and human mesh recovery from egocentric views. EgoGen will be fully
open-sourced, offering a practical solution for creating realistic egocentric
training data and aiming to serve as a useful tool for egocentric computer
vision research. Refer to our project page: https://ego-gen.github.io/.
