The official implementation of our NeurIPS 2024 paper:
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, Hongyang Li
📜 [technical report], 🎬 [video demos], 🤗 [model weights], 🗃️ [OpenDV dataset], 🪧 [poster]
Simulated futures in a wide range of driving scenarios by Vista. Best viewed on demo page.
Vista is a generalizable driving world model that can:
- Predict high-fidelity futures in various scenarios.
- Extend its predictions to continuous and long horizons.
- Execute multi-modal actions (steering angles, speeds, commands, trajectories, goal points).
- Provide rewards for different actions without accessing ground truth actions.
Important
There is an error in merging the EMA weights of the previously uploaded model. Please download the latest model below.
- [2024/06/06] We released the model weights v1.0 at Hugging Face and Google Drive.
- [2024/06/04] We released the installation, training, and sampling scripts.
- [2024/05/28] We released the implementation of our model.
- [2024/05/28] We released our paper on arXiv.
- New model weights trained with a larger batch size ane more iterations.
- Memory efficient training and sampling.
- Online demo for interaction.
Our implementation is based on generative-models from Stability AI. Thanks for their great open-source work!
If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.
@inproceedings{gao2024vista,
title={Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability},
author={Shenyuan Gao and Jiazhi Yang and Li Chen and Kashyap Chitta and Yihang Qiu and Andreas Geiger and Jun Zhang and Hongyang Li},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2024}
}
@inproceedings{yang2024genad,
title={Generalized Predictive Model for Autonomous Driving},
author={Jiazhi Yang and Shenyuan Gao and Yihang Qiu and Li Chen and Tianyu Li and Bo Dai and Kashyap Chitta and Penghao Wu and Jia Zeng and Ping Luo and Jun Zhang and Andreas Geiger and Yu Qiao and Hongyang Li},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}All content in this repository are under the Apache-2.0 license.
