Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910376142045184 |
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| author | Wang, Fu-Yun Wu, Xiaoshi Huang, Zhaoyang Shi, Xiaoyu Shen, Dazhong Song, Guanglu Liu, Yu Li, Hongsheng |
| author_facet | Wang, Fu-Yun Wu, Xiaoshi Huang, Zhaoyang Shi, Xiaoyu Shen, Dazhong Song, Guanglu Liu, Yu Li, Hongsheng |
| contents | Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting. MOTIA comprises two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves conducting efficient and effective pseudo outpainting learning on the single-shot source video. This process encourages the model to identify and learn patterns within the source video, as well as bridging the gap between standard generative processes and outpainting. The subsequent phase, pattern-aware outpainting, is dedicated to the generalization of these learned patterns to generate outpainting outcomes. Additional strategies including spatial-aware insertion and noise travel are proposed to better leverage the diffusion model's generative prior and the acquired video patterns from source videos. Extensive evaluations underscore MOTIA's superiority, outperforming existing state-of-the-art methods in widely recognized benchmarks. Notably, these advancements are achieved without necessitating extensive, task-specific tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13745 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation Wang, Fu-Yun Wu, Xiaoshi Huang, Zhaoyang Shi, Xiaoyu Shen, Dazhong Song, Guanglu Liu, Yu Li, Hongsheng Computer Vision and Pattern Recognition Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting. MOTIA comprises two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves conducting efficient and effective pseudo outpainting learning on the single-shot source video. This process encourages the model to identify and learn patterns within the source video, as well as bridging the gap between standard generative processes and outpainting. The subsequent phase, pattern-aware outpainting, is dedicated to the generalization of these learned patterns to generate outpainting outcomes. Additional strategies including spatial-aware insertion and noise travel are proposed to better leverage the diffusion model's generative prior and the acquired video patterns from source videos. Extensive evaluations underscore MOTIA's superiority, outperforming existing state-of-the-art methods in widely recognized benchmarks. Notably, these advancements are achieved without necessitating extensive, task-specific tuning. |
| title | Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.13745 |