EgoGen: An Egocentric Synthetic Data Generator

EgoGen is a synthetic data generator designed for egocentric perception tasks, producing accurate ground‑truth training data by simulating natural human movements that steer virtual cameras. Its core is a human motion synthesis model that uses egocentric visual inputs to sense 3D environments, combined with collision‑avoiding motion primitives and a two‑stage reinforcement learning approach for closed‑loop perception and movement. The system eliminates the need for pre‑defined global paths, works in dynamic settings, and supports tasks such as mapping, localization, camera tracking, and human mesh recovery from first‑person views.

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• computer-vision
• reinforcement-learning
• robotics