MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

• URL: http://arxiv.org/abs/2412.15322v2
• Date: Mon, 07 Apr 2025 18:00:00 GMT
• Title: MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
• Authors: Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji,
• Abstract summary: We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio.<n> MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples.<n> MMAudio achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance.
• Score: 56.01110988816489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code and demo are available at: https://hkchengrex.github.io/MMAudio

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