MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control
While recent generative models produce high-fidelity videos, they struggle with the complex narrative control required for coherent multi-shot audio-visual generation. Existing methods suffer from temporal misalignment, limited controllability, and incomplete scripting. In this paper, we propose MAVIN, the first framework for multi-shot audio-visual generation with customized narrative control. To resolve temporal misalignment, we propose boundary-aware attention, which leverages hierarchical captions and boundary-aware token routing to render audio-visual elements within their respective temporal boundaries. To improve the controllability for multi-subject scenarios, we propose ID-aware ...
| Authors | Kaiqi Liu, Yunyao Mao, Ziqi Cai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 13 July 2026 |
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