Native Audio-Visual Alignment for Generation

Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual and acoustic content. Existing open-source methods mainly follow either dual-tower designs, which generate audio and video in separate streams and rely on posterior alignment, or fully unified tri-modal designs, which mix textual context, audio, and video in a single shared space. These paradigms may either weaken fine-grained audio-video co-evolution or couple semantic conditioning with low-level synchronization. We propose NAVA, a Native Audio-Visual Alignment framework that formulates generation as context-conditioned native audio-visual alignment. NAVA first establishes audio-video correspondence in a dedicated alignment space and then applies context as external conditioning to guide the aligned representation. We instantiate this formulation with an Align-then-Fuse MMDiT architecture, which progressively bridges modality-aware alignment and unified audio-video denoising. To support controllable speech generation, we further introduce Timbre-in-Context Conditioning, which binds reference timbre cues to corresponding speech spans through the context pathway. Experiments on Verse-Bench and the Seed-TTS benchmark demonstrate that NAVA achieves superior audio-visual synchronization and video quality, competitive audio quality, and substantially improved reference-timbre controllability with only 6.2B parameters.