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Large-scale multilingual audio visual dubbing

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arxiv 2011.03530 v1 pith:TMSR36C5 submitted 2020-11-06 cs.CV cs.SDeess.AS

classification cs.CVcs.SDeess.AS
keywords translatedaudiolanguagespeakersystemdubbinglarge-scaletarget
verification ladder T0 review T1 audit T2 compute T3 formal

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We describe a system for large-scale audiovisual translation and dubbing, which translates videos from one language to another. The source language's speech content is transcribed to text, translated, and automatically synthesized into target language speech using the original speaker's voice. The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language. The audio and visual translation subsystems each contain a large-scale generic synthesis model trained on thousands of hours of data in the corresponding domain. These generic models are fine-tuned to a specific speaker before translation, either using an auxiliary corpus of data from the target speaker, or using the video to be translated itself as the input to the fine-tuning process. This report gives an architectural overview of the full system, as well as an in-depth discussion of the video dubbing component. The role of the audio and text components in relation to the full system is outlined, but their design is not discussed in detail. Translated and dubbed demo videos generated using our system can be viewed at https://www.youtube.com/playlist?list=PLSi232j2ZA6_1Exhof5vndzyfbxAhhEs5

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Human Motion Video Generation: A Survey

    cs.CV 2025-09 conditional novelty 4.0 of 10

    A comprehensive survey with a five-phase pipeline model for human motion video generation, covering over 200 papers and adding a new benchmark comparison of nine pose-guided methods.

  2. VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization

    cs.CV 2024-12 conditional novelty 4.0 of 10

    VQTalker learns a discrete facial-motion codebook via GRFSQ and generates talking heads from speech tokens, reporting improved lip sync on non-Indo-European languages at lower bitrate.

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