arXiv:2505.16279v1 Announce Type: new
Abstract: Current movie dubbing technology can produce the desired speech using a reference voice and input video, maintaining perfect synchronization with the visuals while effectively conveying the intended emotions. However, crucial aspects of movie dubbing, including adaptation to various dubbing styles, effective handling of dialogue, narration, and monologues, as well as consideration of subtle details such as speaker age and gender, remain insufficiently explored. To tackle these challenges, we introduce a multi-modal generative framework. First, it utilizes a multi-modal large vision-language model (VLM) to analyze visual inputs, enabling the recognition of dubbing types and fine-grained attributes. Second, it produces high-quality dubbing using large speech generation models, guided by multi-modal inputs. Additionally, a movie dubbing dataset with annotations for dubbing types and subtle details is constructed to enhance movie understanding and improve dubbing quality for the proposed multi-modal framework. Experimental results across multiple benchmark datasets show superior performance compared to state-of-the-art (SOTA) methods. In details, the LSE-D, SPK-SIM, EMO-SIM, and MCD exhibit improvements of up to 1.09%, 8.80%, 19.08%, and 18.74%, respectively.
Expert Commentary: Multi-Modal Generative Framework for Movie Dubbing
The introduction of a multi-modal generative framework for movie dubbing represents a significant advancement in the field of multimedia information systems. This innovative approach combines vision and language models to analyze visual inputs, enabling the recognition of dubbing types and fine-grained attributes. By utilizing large speech generation models guided by multi-modal inputs, the framework can produce high-quality dubbing that maintains perfect synchronization with the visuals while effectively conveying the intended emotions.
One of the key strengths of this framework is its ability to address crucial aspects of movie dubbing that have remained insufficiently explored, such as adaptation to various dubbing styles, effective handling of dialogue, narration, and monologues, and consideration of subtle details like speaker age and gender. By constructing a movie dubbing dataset with annotations for dubbing types and subtle details, the framework not only enhances movie understanding but also improves dubbing quality across multiple benchmark datasets.
From an interdisciplinary perspective, this research at the intersection of vision and language modeling, speech generation, and multimedia information systems demonstrates the interconnected nature of emerging technologies like Animations, Artificial Reality, Augmented Reality, and Virtual Realities. The ability to generate high-quality dubbing can have implications for various applications beyond traditional movie dubbing, such as interactive multimedia experiences, virtual reality simulations, and educational tools.
Key Takeaways:
- The multi-modal generative framework combines vision and language models for analyzing visual inputs in movie dubbing.
- This approach enhances dubbing quality by effectively conveying emotions and maintaining synchronization with visuals.
- The framework addresses crucial aspects of movie dubbing that have been insufficiently explored in previous research.
- Interdisciplinary connections to multimedia information systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities highlight the broader implications of this research.