OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation
Recent generative models are moving beyond silent video or standalone audio synthesis toward the joint generation of synchronized audio and video. Despite this progress, jointly generating audio and video with fine-grained cross-modal correspondence remains challenging due to their fundamental structural differences. Most existing methods use audio and video VAEs trained separately. As a result, the two latent spaces lack cross-modal alignment, leaving the downstream generative model to learn cross-modal synchronization from scratch. We present OmniVAE, a jointly trained audio-video VAE that learns fine-grained semantic alignment between audio and video latent representations. Beyond reco...
| Authors | Jun Zhan, Chen Yang, Yitian Gong et al. |
|---|---|
| Primary category | cs.SD |
| Published | 26 July 2026 |
| Last updated at source | 31 July 2026 |
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