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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.20974 |
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| _version_ | 1866909454848491520 |
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| author | Qiu, Di Chen, Zheng Wang, Rui Fan, Mingyuan Yu, Changqian Huang, Junshi Wen, Xiang |
| author_facet | Qiu, Di Chen, Zheng Wang, Rui Fan, Mingyuan Yu, Changqian Huang, Junshi Wen, Xiang |
| contents | Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple yet effective tuning-free framework for character video synthesis, named MovieCharacter, designed to streamline the synthesis process while ensuring high-quality outcomes. Our framework decomposes the synthesis task into distinct, manageable modules: character segmentation and tracking, video object removal, character motion imitation, and video composition. This modular design not only facilitates flexible customization but also ensures that each component operates collaboratively to effectively meet user needs. By leveraging existing open-source models and integrating well-established techniques, MovieCharacter achieves impressive synthesis results without necessitating substantial resources or proprietary datasets. Experimental results demonstrate that our framework enhances the efficiency, accessibility, and adaptability of character video synthesis, paving the way for broader creative and interactive applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis Qiu, Di Chen, Zheng Wang, Rui Fan, Mingyuan Yu, Changqian Huang, Junshi Wen, Xiang Computer Vision and Pattern Recognition Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple yet effective tuning-free framework for character video synthesis, named MovieCharacter, designed to streamline the synthesis process while ensuring high-quality outcomes. Our framework decomposes the synthesis task into distinct, manageable modules: character segmentation and tracking, video object removal, character motion imitation, and video composition. This modular design not only facilitates flexible customization but also ensures that each component operates collaboratively to effectively meet user needs. By leveraging existing open-source models and integrating well-established techniques, MovieCharacter achieves impressive synthesis results without necessitating substantial resources or proprietary datasets. Experimental results demonstrate that our framework enhances the efficiency, accessibility, and adaptability of character video synthesis, paving the way for broader creative and interactive applications. |
| title | MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.20974 |