Summary of Taming Multimodal Joint Training For High-quality Video-to-audio Synthesis, by Ho Kei Cheng et al.
Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
by Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multimodal joint training framework called MMAudio is proposed to synthesize high-quality and synchronized audio from video and optional text conditions. Unlike single-modality training conditioned on limited video data, MMAudio is trained jointly with larger-scale text-audio data to learn to generate semantically aligned audio samples. The framework achieves new state-of-the-art performance in terms of audio quality, semantic alignment, and audio-visual synchronization among public models, while maintaining a low inference time and parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to create an AI model that can turn video into high-quality audio! This model is special because it uses both the video and text data together to make sure the audio sounds good. It’s really fast and only needs a small amount of computer power. The model also does well when we give it just text, which is cool! |
Keywords
» Artificial intelligence » Alignment » Inference
