MOVA: Towards Scalable and Synchronized Video-Audio Generation
Authors: name: SII-OpenMOSS Team, name: Donghua Yu, name: Mingshu Chen, name: Qi Chen, name: Qi Luo, name: Qianyi Wu, name: Qinyuan Cheng, name: Ruixiao Li, name: Tianyi Liang, name: Wenbo Zhang, name: Wenming Tu, name: Xiangyu Peng, name: Yang Gao, name: Yanru Huo, name: Ying Zhu, name: Yinze Luo, name: Yiyang Zhang, name: Yuerong Song, name: Zhe Xu, name: Zhiyu Zhang, name: Chenchen Yang, name: Cheng Chang, name: Chushu Zhou, name: Hanfu Chen, name: Hongnan Ma, name: Jiaxi Li, name: Jingqi Tong, name: Junxi Liu, name: Ke Chen, name: Shimin Li, name: Songlin Wang, name: Wei Jiang, name: Zhaoye Fei, name: Zhiyuan Ning, name: Chunguo Li, name: Chenhui Li, name: Ziwei He, name: Zengfeng Huang, name: Xie Chen, name: Xipeng Qiu
Organization: OpenMOSS

Audio is indispensable for real-world video, yet generation models have largely overlooked audio components. Current approaches to producing audio-visual content often rely on cascaded pipelines, which increase cost, accumulate errors, and degrade overall quality. While systems such as Veo 3 and Sora 2 emphasize the value of simultaneous generation, joint multimodal modeling introduces unique challenges in architecture, data, and training. Moreover, the closed-source nature of existing systems limits progress in the field. In this work, we introduce MOVA (MOSS Video and Audio), an open-source model capable of generating high-quality, synchronized audio-visual content, including realistic lip-synced speech, environment-aware sound effects, and content-aligned music. MOVA employs a Mixture-of-Experts (MoE) architecture, with a total of 32B parameters, of which 18B are active during inference. It supports IT2VA (Image-Text to Video-Audio) generation task. By releasing the model weights and code, we aim to advance research and foster a vibrant community of creators. The released codebase features comprehensive support for efficient inference, LoRA fine-tuning, and prompt enhancement.