Taming Multimodal Joint Training for High-Quality Video-to-Audio
  Synthesis
Authors: name: Ho Kei Cheng, name: Masato Ishii, name: Akio Hayakawa, name: Takashi Shibuya, name: Alexander Schwing, name: Yuki Mitsufuji
Organization: Sony

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