OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency due to their bidirectional attention dependencies, hindering real-time applications. We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autoregressive generator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the extreme temporal asymmetry between modalities and the resulting token sparsity. We address the inherent information density gap by introducing an Asymmetric Block-Causal Alignment with a zero-truncation Glob...
| Authors | Yaofeng Su, Yuming Li, Zeyue Xue et al. |
|---|---|
| Primary category | cs.MM |
| Published | 12 March 2026 |
| Last updated at source | 13 March 2026 |
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