Summary of V-express: Conditional Dropout For Progressive Training Of Portrait Video Generation, by Cong Wang et al.
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
by Cong Wang, Kuan Tian, Jun Zhang, Yonghang Guan, Feng Luo, Fei Shen, Zhiwei Jiang, Qing Gu, Xiao Han, Wei Yang
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 V-Express method tackles the challenge of balancing control signals in portrait video generation, particularly when weak signals like audio are overshadowed by stronger ones like facial pose and reference image. By leveraging generative models and conditional dropout operations, V-Express enables effective control through progressive training. This approach simultaneously considers multiple conditions, such as facial pose, reference image, and audio, to generate high-quality portrait videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers focus on improving the quality of portrait videos generated from single images. They use generative models and adapters to enhance video generation, but note that weak control signals can be overshadowed by stronger ones. To solve this problem, they develop V-Express, a simple method that balances different control signals. This helps generate high-quality videos that take into account multiple conditions. |
Keywords
» Artificial intelligence » Dropout
