Summary of Gendds: Generating Diverse Driving Video Scenarios with Prompt-to-video Generative Model, by Yongjie Fu et al.
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model
by Yongjie Fu, Yunlong Li, Xuan Di
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes GenDDS, a novel approach for generating diverse driving scenarios using Stable Diffusion XL (SDXL), an advanced latent diffusion model. The methodology involves descriptive prompts to guide the synthesis process, aiming to produce realistic and varied driving scenarios. By combining SDXL with ControlNet and Hotshot-XL computer vision techniques, the authors create a pipeline for video generation and train the model on the KITTI dataset. Experiments demonstrate that GenDDS can generate high-quality driving videos that closely replicate real-world driving scenarios’ complexity and variability. This research contributes to sophisticated training data development for autonomous driving systems and opens avenues for virtual environments creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where self-driving cars can learn from realistic simulations, just like humans do. To make this happen, researchers developed GenDDS, a new way to create diverse driving scenarios using advanced computer models. They used a powerful tool called Stable Diffusion XL (SDXL) and added some clever ideas to generate videos that look and feel like real-world driving. The team tested their approach on the KITTI dataset and showed that it can produce high-quality videos that mimic real-life driving situations. This breakthrough will help develop more realistic training data for self-driving cars, making them safer and better equipped to handle different scenarios. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
