Virbo: Multimodal Multilingual Avatar Video Generation in Digital Marketing
<|reference_start|>Virbo: Multimodal Multilingual Avatar Video Generation in Digital Marketing: With the widespread popularity of internet celebrity marketing all over the world, short video production has gradually become a popular way of presenting products information. However, the traditional video production industry usually includes series of procedures as script writing, video filming in a professional studio, video clipping, special effects rendering, customized post-processing, and so forth. Not to mention that multilingual videos is not accessible for those who could not speak multilingual languages. These complicated procedures usually needs a professional team to complete, and this made short video production costly in both time and money. This paper presents an intelligent system that supports the automatic generation of talking avatar videos, namely Virbo. With simply a user-specified script, Virbo could use a deep generative model to generate a target talking videos. Meanwhile, the system also supports multimodal inputs to customize the video with specified face, specified voice and special effects. This system also integrated a multilingual customization module that supports generate multilingual talking avatar videos in a batch with hundreds of delicate templates and creative special effects. Through a series of user studies and demo tests, we found that Virbo can generate talking avatar videos that maintained a high quality of videos as those from a professional team while reducing the entire production costs significantly. This intelligent system will effectively promote the video production industry and facilitate the internet marketing neglecting of language barriers and cost challenges.<|reference_end|>
@article{zhang2024virbo:,
    title={Virbo: Multimodal Multilingual Avatar Video Generation in Digital
  Marketing},
    author={Juan Zhang, Jiahao Chen, Cheng Wang, Zhiwang Yu, Tangquan Qi, Can Liu,
  Di Wu},
    journal={arXiv preprint arXiv:2403.11700},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11700},
    primaryClass={cs.MM}
}