Summary of Humanvid: Demystifying Training Data For Camera-controllable Human Image Animation, by Zhenzhi Wang et al.
HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation
by Zhenzhi Wang, Yixuan Li, Yanhong Zeng, Youqing Fang, Yuwei Guo, Wenran Liu, Jing Tan, Kai Chen, Tianfan Xue, Bo Dai, Dahua Lin
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents HumanVid, a large-scale high-quality dataset for human image animation, which combines real-world and synthetic data. The dataset is designed to facilitate fair and transparent benchmarking in the field of human image animation, where recent approaches have been hindered by the lack of accessible training data. The authors introduce a rule-based camera trajectory generation method that enables precise camera motion annotation, which can rarely be found in real-world data. They also develop a baseline model called CamAnimate that considers both human and camera motions as conditions. Through extensive experimentation, they demonstrate that this simple baseline training on HumanVid achieves state-of-the-art performance in controlling both human pose and camera motions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating better videos from pictures of people. It’s like making a movie, but instead of using real actors, you use photos. The problem is that most methods for doing this don’t work well because they need special data to train them. This data is hard to get and makes it hard to compare different methods. The authors of this paper created a big dataset called HumanVid that includes both real-world videos and fake ones made in a computer. They also developed a way to control the camera movements in these videos, which is important because it makes the video more realistic. By using their dataset and model, they were able to make videos that look really good and are easy to control. |
Keywords
* Artificial intelligence * Synthetic data
