Title: Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation
Primary area: generative models
Keywords: Artificial Intelligence, Deep Learning, Computer Vision, Video Generation, Audio Generation

Abstract: Audio–video (AV) generation has often relied on complex multi-stage architectures or sequential synthesis of sound and visuals. We introduce Ovi, a unified paradigm for audio–video generation that models the two modalities as a single generative process. By using blockwise cross-modal fusion of twin-DiT modules, Ovi achieves natural synchronization and removes the need for separate pipelines or post hoc alignment. To facilitate fine-grained multimodal fusion modeling, we initialize an audio tower with an architecture identical to that of a strong pretrained video model. Trained from scratch on hundreds of thousands of hours of raw audio, the audio tower learns to generate realistic sound effects, as well as speech that conveys rich speaker identity and emotion. Fusion is obtained by jointly training the identical video and audio towers via blockwise exchange of timing (via scaled-RoPE embeddings) and semantics (through bidirectional cross-attention) on a vast video corpus. Our model enables cinematic storytelling with natural speech and accurate, context-matched sound effects, producing movie-grade video clips. We will open-source our model to foster further research.

TL;DR: Ovi is a Twin-DiT generative model that jointly generates perfectly synchronized video and audio