We present an innovative generative artificial intelligence framework that converts rap lyrics into synchronized vocal performances and full body animations, including gestures and facial expressions. Based on the RapVerse dataset, the system uses a unified token representation for text, audio and motion, enabling direct mapping of words to coherent sounds and movements.
The architecture is modular and combines specialized tokenizers for motion and vocals with a large autoregressive model that generates expressive multimodal outputs from a lyrical input. This design facilitates voice synthesis with natural intonation, the rhythmic timing of rap and the production of full-body animations that respect the prosody and emphasis of the text.
Among the technical features, the use of unified tokens representing phonemes, vocal timbres and motion segments stands out, allowing seamless integration between audio and animation. Modular tokenizers allow adjusting the granularity of motion and voice, while the autoregressive model ensures temporal coherence and dynamic expressiveness in complete performances.
The applications of this technology range from the creation of automatically generated music videos to tools for artists who wish to prototype performances, virtual assistants with musical personality and interactive entertainment systems. Furthermore, by generating gestures and facial expressions synchronized with the lyrics, the perception of authenticity and emotional connection in the audience is improved.
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