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arXiv · 2412.03758

ARCON: Advancing Auto-Regressive Continuation for Driving Videos

Ruibo Ming·Jingwei Wu·Zhewei Huang·Zhuoxuan Ju·Jianming HU·Lihui Peng·Shuchang Zhou
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Abstract

Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for video continuation, a task essential for building world models and predicting future frames. We introduce ARCON, a scheme that alternates between generating semantic and RGB tokens, allowing the LVM to explicitly learn high-level structural video information. We find high consistency in the RGB images and semantic maps generated without special design. Moreover, we employ an optical flow-based texture stitching method to enhance visual quality. Experiments in autonomous driving scenarios show that our model can consistently generate long videos.

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Ruibo Ming, Jingwei Wu, Zhewei Huang, Zhuoxuan Ju, Jianming HU, Lihui Peng, Shuchang Zhou. 2024-12-04. ARCON: Advancing Auto-Regressive Continuation for Driving Videos. https://arxiv.org/abs/2412.03758

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