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The paper introduces MOVA, an open-source Mixture-of-Experts (MoE) model with 32B parameters (18B active) designed for synchronized video and audio generation. MOVA addresses the limitations of cascaded pipelines and closed-source systems by enabling simultaneous generation of high-quality audio-visual content, including lip-synced speech and environment-aware sound effects. The model supports Image-Text to Video-Audio (IT2VA) generation and is released with code for efficient inference, LoRA fine-tuning, and prompt enhancement.
Open-source MOVA lets you generate synchronized, high-quality video and audio—including realistic lip sync—without relying on closed-source systems.
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.
Tsinghua AI