Zeqi Xiao1
Yushi Lan1
Yifan Zhou1
Wenqi Ouyang1
Shuai Yang2
Yanhong Zeng3
Xingang Pan1
1S-Lab, Nanyang Technological University,
2Wangxuan Institute of Computer Technology, Peking University,
3Shanghai AI Laboratry
demo.1.1.mp4
conda create python=3.10 -n worldmem
conda activate worldmem
pip install -r requirements.txt
python app.py
- Release inference models and weights;
- Release training pipeline on Minecraft;
- Release training data on Minecraft;
If you find our work helpful, please cite:
@misc{xiao2025worldmemlongtermconsistentworld,
title={WORLDMEM: Long-term Consistent World Simulation with Memory},
author={Zeqi Xiao and Yushi Lan and Yifan Zhou and Wenqi Ouyang and Shuai Yang and Yanhong Zeng and Xingang Pan},
year={2025},
eprint={2504.12369},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.12369},
}
- Diffusion Forcing: Diffusion Forcing provides flexible training and inference strategies for our methods.
- Minedojo: We collect our Minecraft dataset from Minedojo.
- Open-oasis: Our model architecture is based on Open-oasis. We also use pretrained VAE and DiT weight from it.