Papers › MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video-to-Sound Generation | VGG-Sound | MMAudio-S-16kHz | FAD | 0.79 | #1 of 8 | Archive leaderboard | report |
| Video-to-Sound Generation | VGG-Sound | MMAudio-S-16kHz | FD | 5.22 | #1 of 8 | Archive leaderboard | report |
| Video-to-Sound Generation | VGG-Sound | MMAudio-L-44.1kHz | FAD | 0.97 | #3 of 8 | Archive leaderboard | report |
| Video-to-Sound Generation | VGG-Sound | MMAudio-L-44.1kHz | FD | 4.72 | #3 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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