Summary of Animatelcm: Computation-efficient Personalized Style Video Generation Without Personalized Video Data, by Fu-yun Wang et al.
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
by Fu-Yun Wang, Zhaoyang Huang, Weikang Bian, Xiaoyu Shi, Keqiang Sun, Guanglu Song, Yu Liu, Hongsheng Li
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method enables efficient personalized style video generation without requiring access to personalized video data. It achieves a significant reduction in generation time, from 25 seconds to around 1 second, while maintaining performance comparable to larger video diffusion models. The dual-level decoupling learning approach is key to this success, separating the learning of video style from acceleration and image/video motion generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This method makes personalized style video generation possible without needing any personalized video data. It’s really fast too – down to just 1 second! The way it works is clever, splitting the process into two parts: learning the style and accelerating the generation. This makes training more efficient and helps with using lower-quality video data. |
Keywords
* Artificial intelligence * Diffusion
