Summary of Zero-shot High-fidelity and Pose-controllable Character Animation, by Bingwen Zhu et al.
Zero-shot High-fidelity and Pose-controllable Character Animation
by Bingwen Zhu, Fanyi Wang, Tianyi Lu, Peng Liu, Jingwen Su, Jinxiu Liu, Yanhao Zhang, Zuxuan Wu, Guo-Jun Qi, Yu-Gang Jiang
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper proposes PoseAnimate, a novel zero-shot image-to-video (I2V) framework for character animation that addresses limitations in existing approaches. The framework consists of three key components: the Pose-Aware Control Module (PACM), Dual Consistency Attention Module (DCAM), and Mask-Guided Decoupling Module (MGDM). These modules work together to preserve character-independent content, maintain precise alignment of actions, enhance temporal consistency, retain character identity and intricate background details, and improve animation fidelity. The paper also introduces the Pose Alignment Transition Algorithm (PATA) to ensure smooth action transition. Experimental results show that PoseAnimate outperforms state-of-the-art training-based methods in terms of character consistency and detail fidelity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates videos from single images, making characters look consistent and details clear. It’s hard for computers to do this because they need lots of video data to train. The new method, called PoseAnimate, has three parts that help it work better: keeping track of character poses, paying attention to important details, and separating the character from the background. This makes the videos look more realistic and smooth. The results show that PoseAnimate is better than other methods at making consistent characters and detailed animations. |
Keywords
» Artificial intelligence » Alignment » Attention » Mask » Zero shot
