MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset
<|reference_start|>MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset: Recent studies in speech-driven 3D talking head generation have achieved convincing results in verbal articulations. However, generating accurate lip-syncs degrades when applied to input speech in other languages, possibly due to the lack of datasets covering a broad spectrum of facial movements across languages. In this work, we introduce a novel task to generate 3D talking heads from speeches of diverse languages. We collect a new multilingual 2D video dataset comprising over 420 hours of talking videos in 20 languages. With our proposed dataset, we present a multilingually enhanced model that incorporates language-specific style embeddings, enabling it to capture the unique mouth movements associated with each language. Additionally, we present a metric for assessing lip-sync accuracy in multilingual settings. We demonstrate that training a 3D talking head model with our proposed dataset significantly enhances its multilingual performance. Codes and datasets are available at https://multi-talk.github.io/.<|reference_end|>
@article{sung-bin2024multitalk:,
    title={MultiTalk: Enhancing 3D Talking Head Generation Across Languages with
  Multilingual Video Dataset},
    author={Kim Sung-Bin, Lee Chae-Yeon, Gihun Son, Oh Hyun-Bin, Janghoon Ju,
  Suekyeong Nam, Tae-Hyun Oh},
    journal={arXiv preprint arXiv:2406.14272},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14272},
    primaryClass={cs.CV cs.GR}
}