AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915972969922560 |
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| author | Zheng, Haojie Weng, Shuchen Liu, Jingqi Yang, Siqi Shi, Boxin Wang, Xinlong |
| author_facet | Zheng, Haojie Weng, Shuchen Liu, Jingqi Yang, Siqi Shi, Boxin Wang, Xinlong |
| contents | Recent advancements in video generation highlight that realistic audio-visual synchronization is crucial for engaging content creation. However, existing video editing methods largely overlook audio-visual synchronization and lack the fine-grained spatial and temporal controllability required for precise instance-level edits. In this paper, we propose AVI-Edit, a framework for audio-sync video instance editing. We propose a granularity-aware mask refiner that iteratively refines coarse user-provided masks into precise instance-level regions. We further design a self-feedback audio agent to curate high-quality audio guidance, providing fine-grained temporal control. To facilitate this task, we additionally construct a large-scale dataset with instance-centric correspondence and comprehensive annotations. Extensive experiments demonstrate that AVI-Edit outperforms state-of-the-art methods in visual quality, condition following, and audio-visual synchronization. Project page: https://hjzheng.net/projects/AVI-Edit/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner Zheng, Haojie Weng, Shuchen Liu, Jingqi Yang, Siqi Shi, Boxin Wang, Xinlong Computer Vision and Pattern Recognition Recent advancements in video generation highlight that realistic audio-visual synchronization is crucial for engaging content creation. However, existing video editing methods largely overlook audio-visual synchronization and lack the fine-grained spatial and temporal controllability required for precise instance-level edits. In this paper, we propose AVI-Edit, a framework for audio-sync video instance editing. We propose a granularity-aware mask refiner that iteratively refines coarse user-provided masks into precise instance-level regions. We further design a self-feedback audio agent to curate high-quality audio guidance, providing fine-grained temporal control. To facilitate this task, we additionally construct a large-scale dataset with instance-centric correspondence and comprehensive annotations. Extensive experiments demonstrate that AVI-Edit outperforms state-of-the-art methods in visual quality, condition following, and audio-visual synchronization. Project page: https://hjzheng.net/projects/AVI-Edit/. |
| title | AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.10571 |