# Glad: A Streaming Scene Generator for Autonomous Driving

arXiv: https://arxiv.org/abs/2503.00045 · HTML: https://mlsanity.com/paper/2503.00045/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2025.

## Built on by (most cited first, 17 of 17)

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- [DiVE: Efficient Multi-View Driving Scenes Generation Based on Video Diffusion Transformer](https://mlsanity.com/paper/2504.19614/) (2025)
- [STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation](https://mlsanity.com/paper/2506.13138/) (2025)
- [OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving](https://mlsanity.com/paper/2512.14225/) (2025)
- [Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms](https://mlsanity.com/paper/2603.28489/) (2026)
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- [Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges](https://mlsanity.com/paper/2603.09086/) (2026)
- [CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving](https://mlsanity.com/paper/2511.13297/) (2025)
- [CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal Reconstruction Model for Autonomous Driving](https://mlsanity.com/paper/2510.07944/) (2025)
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- [Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling](https://mlsanity.com/paper/2508.06805/) (2025)
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- [VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation](https://mlsanity.com/paper/2508.06819/) (2025)
- [AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond](https://mlsanity.com/paper/2605.26113/) (2026)

## Abstract (arXiv)

The generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researchers have explored employing neural radiance fields or diffusion models to generate novel views or synthetic data under driving scenes. However, these approaches suffer from unseen scenes or restricted video length, thus lacking sufficient adaptability for data generation and simulation. To address these issues, we propose a simple yet effective framework, named Glad, to generate video data in a frame-by-frame style. To ensure the temporal consistency of synthetic video, we introduce a latent variable propagation module, which views the latent features of previous frame as noise prior and injects it into the latent features of current frame. In addition, we design a streaming data sampler to orderly sample the original image in a video clip at continuous iterations. Given the reference frame, our Glad can be viewed as a streaming simulator by generating the videos for specific scenes. Extensive experiments are performed on the widely-used nuScenes dataset. Experimental results demonstrate that our proposed Glad achieves promising performance, serving as a strong baseline for online video generation. We will release the source code and models publicly.