Summary of Cstalk: Correlation Supervised Speech-driven 3d Emotional Facial Animation Generation, by Xiangyu Liang et al.
CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation
by Xiangyu Liang, Wenlin Zhuang, Tianyong Wang, Guangxing Geng, Guangyue Geng, Haifeng Xia, Siyu Xia
First submitted to arxiv on: 29 Apr 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 This paper proposes a novel approach, called CSTalk (Correlation Supervised), to generate realistic 3D facial animations driven by speech. The authors aim to overcome current limitations in data, lip alignment, and naturalness of facial expressions. By modeling correlations among facial regions and supervising the training process, CSTalk generates expressive and human-like animations. To further enhance control and variety, the paper employs a rich set of parameters based on the metahuman character model and captures a dataset for five emotions. Experimental results show that CSTalk outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates computer animations that look like real people talking. Right now, these animations can look stiff or fake because they don’t match how humans move their faces naturally. The researchers developed a new method called CSTalk to make the animations more realistic and expressive. They want to be able to control the emotions and movements of the animation to make it seem more human-like. |
Keywords
» Artificial intelligence » Alignment » Supervised
