ARCON: Advancing Auto-Regressive Continuation for Driving Videos
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929732623269888 |
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| author | Ming, Ruibo Wu, Jingwei Huang, Zhewei Ju, Zhuoxuan HU, Jianming Peng, Lihui Zhou, Shuchang |
| author_facet | Ming, Ruibo Wu, Jingwei Huang, Zhewei Ju, Zhuoxuan HU, Jianming Peng, Lihui Zhou, Shuchang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ARCON: Advancing Auto-Regressive Continuation for Driving Videos Ming, Ruibo Wu, Jingwei Huang, Zhewei Ju, Zhuoxuan HU, Jianming Peng, Lihui Zhou, Shuchang Computer Vision and Pattern Recognition 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. |
| title | ARCON: Advancing Auto-Regressive Continuation for Driving Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.03758 |