Decoupled Diffusion Sparks Adaptive Scene Generation

Yunsong Zhou, Naisheng Ye, William Ljungbergh, Tianyu Li, et al.

• 📬 Primary contact: Yunsong Zhou ( zhouyunsong2017@gmail.com )

🔥 Nexus is a noise-decoupled prediction pipeline designed for adaptive driving scene generation, ensuring both timely reaction⏲️ and goal-directed control🥅.

🌟 Nexus can generate realistic safety-critical driving scenarios by flexibly controlling the future state of a scene, with the assistance of NeRF.

• [2024/04] Nexus paper released.
• [2025/04] Nexus code and data initially released.

1. Highlights
2. News
3. TODO List
4. Getting Started
5. Dataset
6. License and Citation
7. Related Resources

• Guidance tutorial
• Training code
• Nexus & checkpoint
• Initial repo & paper

• Installation
• Try Our Demo 🔥
• Prepare Dataset
• Train and Eval

Nexus-Data is induced from real-world scenarios, in which we can obtain real-world map topology and layout. It also includes hazardous driving behaviors through interactions introduced by adversarial traffic generation. The safety-critical scenarios (on nuPlan dataset) can be obtained through this 🔗data link.

All assets and code in this repository are under the Apache 2.0 license unless specified otherwise. The data is under CC BY-NC-SA 4.0. Please consider citing our paper and project if they help your research.

@article{zhou2024decoupled,
  title={Decoupled Diffusion Sparks Adaptive Scene Generation},
  author={Zhou, Yunsong and Ye, Naisheng and Ljungbergh, William and Li, Tianyu and Yang, Jiazhi and Yang, Zetong and Zhu, Hongzi and Petersson, Christoffer and Li, Hongyang},
  journal={arXiv preprint arXiv:2504.10485},
  year={2025}
}

We acknowledge all the open-source contributors for the following projects to make this work possible:

• GUMP | DiffusionPlanner

• SimGen | Vista | Centaur
• MTGS | OpenLane-V2 | OpenScene