FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model

2026-06-18 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

FrozenDrive is a controllable generative framework designed to enhance synthetic data generation for autonomous driving by addressing key limitations of existing diffusion models. It tackles challenges like maintaining multi-view and temporal consistency, and improving performance under adverse weather or rare scene configurations, without fine-tuning the backbone. FrozenDrive achieves this by preserving pre-trained diffusion model knowledge, conditioning on rich driving-stack signals and text prompts, and introducing knowledge-preserving spatio-temporal attention. This attention mechanism imposes cross-view alignment and temporal coherence in a single pass within a parameter-free frozen diffusion backbone. An additional object-focused constraint improves per-object fidelity for rare categories. The model synthesizes globally coherent multi-view driving scenes from text, outperforming prior baselines, especially in adverse conditions. On nuScenes, FrozenDrive-augmented data significantly improves AD model performance, particularly at night and in rain, demonstrating stronger robustness.

Key takeaway

For autonomous driving (AD) model developers aiming to enhance robustness in challenging conditions, you should consider integrating synthetic data generated by frameworks like FrozenDrive. This approach allows you to create scenario-targeted data, particularly for adverse weather or rare events, without extensive fine-tuning. Leveraging such augmented datasets can significantly improve your AD models' performance, especially at night and in rain, leading to more reliable systems.

Key insights

FrozenDrive generates consistent multi-view driving scenes and augments data using a parameter-free frozen diffusion model.

Principles

• Preserve pre-trained knowledge in generative models.
• Enforce spatio-temporal consistency without fine-tuning.
• Improve fidelity for rare categories via object-focused constraints.

Method

FrozenDrive conditions on driving-stack signals and text prompts, using knowledge-preserving spatio-temporal attention for cross-view and temporal coherence in a single pass within a frozen diffusion backbone. An object-focused constraint enhances rare category fidelity.

In practice

• Augment autonomous driving datasets with adverse conditions.
• Generate multi-view driving scenes from text prompts.
• Improve AD model robustness in challenging scenarios.

Topics

• Autonomous Driving
• Diffusion Models
• Synthetic Data Generation
• Data Augmentation
• Multi-view Consistency
• nuScenes

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.