WorldWander

<div align="center"> <h1> WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation </h1> </div>

📖 Overview

Video diffusion models have recently achieved remarkable progress in realism and controllability. However, achieving seamless video translation across different perspectives, such as first-person (egocentric) and third-person (exocentric), remains underexplored. Bridging these perspectives is crucial for filmmaking, embodied AI, and world models. Motivated by this, we present <b>WorldWander</b>, an in-context learning framework tailored for translating between egocentric and exocentric worlds in video generation. Building upon advanced video diffusion transformers, WorldWander integrates (i) <i>In-Context Perspective Alignment</i> and (ii) <i>Collaborative Position Encoding</i> to efficiently model cross-view synchronization. Overall framework is shown below:

🎓 Bibtex

👋 If you find this code useful for your research, we would appreciate it if you could cite:

@article{song2025worldwander,
  title={WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation},
  author={Song, Quanjian and Song, Yiren and Peng, Kelly and Gao, Yuan and Shou, Mike Zheng},
  journal={arXiv preprint arXiv:2511.22098},
  year={2025}
}