Native Audio-Visual Alignment for Generation

Longbin Ji, Guan Wang, Xuan Wei, Chenye Yang, Xiangrui Liu, Max Zhenyu Zhang, Shuohuan Wang, Yu Sun, Jingzhou He

Native Audio-Visual Alignment for Generation: 31 upvotes on Hugging Face Daily Papers, #11 of 59 papers on 2026-05-29. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual-acoustic content. However, existing open-source methods mainly rely on either dual-tower designs with posterior alignment or fully unified tri-modal designs that mix textual context, audio and video in one shared space. The former weakens fine-grained audio-video co-evolution, while the latter couples semantic conditioning with low-level synchronization. To address these limitations, we propose NAVA, a Native Audio-Visual Alignment framework for joint audio-video generation. NAVA is built upon context-conditioned native audio-visual alignment: it first establishes audio-video correspondence in a dedicated interaction space, and then uses external context to condition the joint denoising process. Specifically, NAVA is instantiated with an Align-then-Fuse MMDiT architecture, which transitions from modality-aware audio-video alignment to modality-shared joint denoising. Furthermore, we introduce Timbre-in-Context Conditioning to associate reference timbre cues with corresponding speech spans to achieve controllable speech timbre. Experiments on Verse-Bench and Seed-TTS, together with a user study, demonstrate that NAVA achieves superior video quality, precise audio-visual synchronization, competitive audio quality, and stronger reference-timbre controllability using only 6.3B parameters.

Paper page on Hugging Face · arXiv

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