CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917617400283136 |
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| author | Zi, Bojia Zhao, Shihao Qi, Xianbiao Wang, Jianan Shi, Yukai Chen, Qianyu Liang, Bin Wong, Kam-Fai Zhang, Lei |
| author_facet | Zi, Bojia Zhao, Shihao Qi, Xianbiao Wang, Jianan Shi, Yukai Chen, Qianyu Liang, Bin Wong, Kam-Fai Zhang, Lei |
| contents | Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contrast to the well-explored domain of text-guided image inpainting. To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility. Specifically, we introduce a simple but efficient motion capture module to preserve motion consistency, and design an instance-aware region selection instead of a random region selection to obtain better textual controllability, and utilize a novel strategy to inject some personalized models into our CoCoCo model and thus obtain better model compatibility. Extensive experiments show that our model can generate high-quality video clips. Meanwhile, our model shows better motion consistency, textual controllability and model compatibility. More details are shown in [cococozibojia.github.io](cococozibojia.github.io). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12035 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility Zi, Bojia Zhao, Shihao Qi, Xianbiao Wang, Jianan Shi, Yukai Chen, Qianyu Liang, Bin Wong, Kam-Fai Zhang, Lei Computer Vision and Pattern Recognition Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contrast to the well-explored domain of text-guided image inpainting. To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility. Specifically, we introduce a simple but efficient motion capture module to preserve motion consistency, and design an instance-aware region selection instead of a random region selection to obtain better textual controllability, and utilize a novel strategy to inject some personalized models into our CoCoCo model and thus obtain better model compatibility. Extensive experiments show that our model can generate high-quality video clips. Meanwhile, our model shows better motion consistency, textual controllability and model compatibility. More details are shown in [cococozibojia.github.io](cococozibojia.github.io). |
| title | CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12035 |