Summary of Singer: Vivid Audio-driven Singing Video Generation with Multi-scale Spectral Diffusion Model, by Yan Li et al.
SINGER: Vivid Audio-driven Singing Video Generation with Multi-scale Spectral Diffusion Model
by Yan Li, Ziya Zhou, Zhiqiang Wang, Wei Xue, Wenhan Luo, Yike Guo
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD)
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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 The proposed SINGER model addresses the limitations of existing talking face video generation models when applied to singing by designing a multi-scale spectral module to learn singing patterns and a spectral-filtering module to learn human behaviors associated with singing audio. The model integrates these modules into a diffusion framework, enhancing singing video generation performance. To facilitate research in this area, an in-the-wild audio-visual singing dataset is collected, demonstrating the SINGER model’s ability to generate vivid singing videos that outperform state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machines create singing videos like humans do. Right now, AI can make talking face videos, but they’re not very good at making singing videos because they don’t understand the differences between talking and singing. The researchers created a new model called SINGER that can learn to recognize patterns in singing audio and human behaviors associated with singing. They also collected a dataset of real-world singing videos to help other scientists work on this problem. |
Keywords
» Artificial intelligence » Diffusion
