arXiv:2408.11593v1 Announce Type: new
Abstract: Automatic Video Dubbing (AVD) aims to take the given script and generate speech that aligns with lip motion and prosody expressiveness. Current AVD models mainly utilize visual information of the current sentence to enhance the prosody of synthesized speech. However, it is crucial to consider whether the prosody of the generated dubbing aligns with the multimodal context, as the dubbing will be combined with the original context in the final video. This aspect has been overlooked in previous studies. To address this issue, we propose a Multimodal Context-aware video Dubbing model, termed textbf{MCDubber}, to convert the modeling object from a single sentence to a longer sequence with context information to ensure the consistency of the global context prosody. MCDubber comprises three main components: (1) A context duration aligner aims to learn the context-aware alignment between the text and lip frames; (2) A context prosody predictor seeks to read the global context visual sequence and predict the context-aware global energy and pitch; (3) A context acoustic decoder ultimately predicts the global context mel-spectrogram with the assistance of adjacent ground-truth mel-spectrograms of the target sentence. Through this process, MCDubber fully considers the influence of multimodal context on the prosody expressiveness of the current sentence when dubbing. The extracted mel-spectrogram belonging to the target sentence from the output context mel-spectrograms is the final required dubbing audio. Extensive experiments on the Chem benchmark dataset demonstrate that our MCDubber significantly improves dubbing expressiveness compared to all advanced baselines. The code and demos are available at https://github.com/XiaoYuanJun-zy/MCDubber.
Analysis of Multimodal Context-aware Video Dubbing Model (MCDubber)
In this article, the authors propose a Multimodal Context-aware Video Dubbing model, known as MCDubber, to address the issue of aligning the prosody of synthesized speech with the multimodal context in video dubbing. They argue that previous Automatic Video Dubbing (AVD) models have overlooked the importance of considering the overall context while enhancing the prosody of the synthesized speech.
MCDubber consists of three main components to ensure the consistency of the global context prosody:
- Context Duration Aligner: This component learns the context-aware alignment between the text and lip frames. By considering the context duration, MCDubber takes into account the temporal relationship between the spoken words and the lip movements, resulting in a more realistic dubbing.
- Context Prosody Predictor: The context prosody predictor reads the global context visual sequence and predicts the context-aware global energy and pitch. By analyzing the visual cues of the context, MCDubber enhances the prosody expressiveness of the synthesized speech to match the overall context, providing a more consistent and natural dubbing experience.
- Context Acoustic Decoder: This component predicts the global context mel-spectrogram by utilizing the adjacent ground-truth mel-spectrograms of the target sentence. The extracted mel-spectrogram from the output context mel-spectrograms serves as the final required dubbing audio. By leveraging the context information, MCDubber ensures that the dubbing aligns with the multimodal context and maintains overall coherence.
The authors emphasize the importance of considering the multimodal context in video dubbing, as the synthesized speech will be combined with the original context in the final video. By taking into account both the visual cues and the temporal relationship between the spoken words and lip movements, MCDubber enhances the expressiveness of the dubbing, resulting in a more immersive and natural viewing experience.
The concepts discussed in this article have a strong connection to the wider field of multimedia information systems. Multimedia information systems deal with the retrieval, storage, and processing of multimedia data, including videos and audio. Automatic Video Dubbing, as a subfield of multimedia information systems, focuses on automatically generating speech that aligns with lip motion and prosody expressiveness. MCDubber adds to this field by considering the multimodal context and incorporating it into the dubbing process.
Furthermore, MCDubber is closely related to the fields of Animations, Artificial Reality (AR), Augmented Reality (AR), and Virtual Realities (VR). These fields aim to create immersive and interactive experiences by combining virtual elements with the real world. In the context of video dubbing, MCDubber ensures that the synthesized speech integrates seamlessly with the original context, enhancing the overall realism of the video. This aligns with the goals of AR and VR, where virtual elements are seamlessly integrated into the real world.
In conclusion, the Multimodal Context-aware Video Dubbing model (MCDubber) proposed in this article addresses the limitation of previous AVD models in considering the multimodal context. By incorporating the context duration, visual cues, and adjacent ground-truth mel-spectrograms, MCDubber enhances the prosody expressiveness of dubbing, resulting in a more consistent and natural viewing experience. The concepts discussed in this article have implications for the wider fields of multimedia information systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities, as they provide insights into improving the integration of virtual elements with real-world contexts.