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Manchery authored Dec 1, 2024
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# iVideoGPT: Interactive VideoGPTs are Scalable World Models (NeurIPS 2024)

[[Website]](https://thuml.github.io/iVideoGPT/) [[Paper]](https://arxiv.org/abs/2405.15223) [[Model]](https://huggingface.co/thuml/ivideogpt-oxe-64-act-free)
[[Website]](https://thuml.github.io/iVideoGPT/) [[Paper]](https://arxiv.org/abs/2405.15223) [[Model]](https://huggingface.co/thuml/ivideogpt-oxe-64-act-free) [[Poster]](https://manchery.github.io/assets/pub/nips2024_ivideogpt/poster.pdf) [[Slides]](https://manchery.github.io/assets/pub/nips2024_ivideogpt/slides.pdf)

This repo provides official code and checkpoints for iVideoGPT, a generic and efficient world model architecture that has been pre-trained on millions of human and robotic manipulation trajectories.

@@ -136,7 +136,7 @@ If you find this project useful, please cite our paper as:
title={iVideoGPT: Interactive VideoGPTs are Scalable World Models},
author={Jialong Wu and Shaofeng Yin and Ningya Feng and Xu He and Dong Li and Jianye Hao and Mingsheng Long},
booktitle={Advances in Neural Information Processing Systems},
year={2024}
year={2024},
}
```

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