InstructDubber: Instruction-based Alignment for Zero-shot Movie Dubbing
Abstract
Movie dubbing seeks to synthesize speech from a given script using a specific voice, while ensuring accurate lip synchronization and emotion-prosody alignment with the character's visual performance. However, existing alignment approaches based on visual features face two key limitations: (1)they rely on complex, handcrafted visual preprocessing pipelines, including facial landmark detection and feature extraction; and (2) they generalize poorly to unseen visual domains, often resulting in degraded alignment and dubbing quality. To address these issues, we propose InstructDubber, a novel instruction-based alignment dubbing method for both robust in-domain and zero-shot movie dubbing. Specifically, we first feed the video, script, and corresponding prompts into a multimodal large language model to generate natural language dubbing instructions regarding the speaking rate and emotion state depicted in the video, which is robust to visual domain variations. Second, we design an instructed duration distilling module to mine discriminative duration cues from speaking rate instructions to predict lip-aligned phoneme-level pronunciation duration. Third, for emotion-prosody alignment, we devise an instructed emotion calibrating module, which finetunes an LLM-based instruction analyzer using ground truth dubbing emotion as supervision and predicts prosody based on the calibrated emotion analysis. Finally, the predicted duration and prosody, together with the script, are fed into the audio decoder to generate video-aligned dubbing. Extensive experiments on three major benchmarks demonstrate that InstructDubber outperforms state-of-the-art approaches across both in-domain and zero-shot scenarios.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
InstructDubber presents a novel approach to zero-shot movie dubbing by integrating instruction-based alignment with multimodal processing, addressing significant challenges in the field. The proposed methodology and experimental validation demonstrate its potential to advance the state-of-the-art in audio-visual synchronization and dubbing quality.
Methodology
The methodology presented in InstructDubber is innovative, leveraging a multimodal large language model (LLM) to generate dubbing instructions that address the limitations of traditional visual feature-based approaches. The use of an instructed duration distilling module and an instructed emotion calibrating module reflects a thoughtful integration of language processing with audio-visual synchronization, which is a significant advancement in the field of movie dubbing. The approach is well-structured, moving from instruction generation to phoneme-level duration prediction and emotion-prosody alignment, showcasing a comprehensive pipeline that is both novel and effective.
Results & evaluation
The experiments conducted on three major benchmarks are extensive and demonstrate a clear performance improvement over state-of-the-art methods in both in-domain and zero-shot scenarios. The paper provides sufficient details on the datasets used, evaluation metrics, and comparative results, which support the claims of superior performance. However, the paper could benefit from more detailed analysis on the specific aspects of the benchmarks that highlight the advantages of the proposed method.
Reproducibility
While the paper outlines the methodology and experiments, it lacks detailed implementation specifics that would aid in reproducibility. There is no mention of code availability or a repository for others to access the implementation, which is a critical aspect for validating the results. The demo URL provides some insight into the application, but further details on the experimental setup would enhance reproducibility.
Limitations
One limitation noted is the reliance on the quality of the input video and script, which could affect the overall dubbing quality. Additionally, the model's performance in highly diverse or unconventional visual domains remains untested, which could limit its applicability. The paper does not address potential biases in the LLM's instruction generation, which could impact the emotional and prosodic accuracy of the dubbing.
Broader impact
The potential applications of InstructDubber extend beyond movie dubbing to include video games, virtual reality, and educational content, where accurate dubbing and emotional alignment are crucial. The approach could significantly enhance user experience in multimedia applications, making it a valuable contribution to the field. Furthermore, the integration of LLMs in multimedia processing opens avenues for future research in multimodal AI, potentially influencing various domains within machine learning. InstructDubber presents a novel approach to zero-shot movie dubbing by integrating instruction-based alignment with multimodal processing, addressing significant challenges in the field. The proposed methodology and experimental validation demonstrate its potential to advance the state-of-the-art in audio-visual synchronization and dubbing quality.
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