Summary of Wem-gan: Wavelet Transform Based Facial Expression Manipulation, by Dongya Sun et al.
WEM-GAN: Wavelet transform based facial expression manipulation
by Dongya Sun, Yunfei Hu, Xianzhe Zhang, Yingsong Hu
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 WEM-GAN, a novel facial expression manipulation method that preserves the details of facial features while transforming expressions to target ones. Unlike previous methods, WEM-GAN uses wavelet-based techniques and a U-net autoencoder backbone to improve detail preservation in the editing process. The model also employs high-frequency domain adversarial loss and residual connections to enhance image generation quality. Experiments on the AffectNet dataset demonstrate that WEM-GAN outperforms existing methods in preserving identity features, editing capability, and image quality, as measured by metrics such as Average Content Distance (ACD) and Expression Distance (ED). This technology has potential applications in fields like facial animation, virtual try-on, and human-computer interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to change someone’s facial expression without affecting their identity. That’s what this paper is all about! The researchers developed a new way to manipulate facial expressions called WEM-GAN. Instead of just changing the face, they wanted to keep the details that make it unique. To do this, they used special techniques and a machine learning model. They tested their method on a big dataset and found that it worked really well. It can be used in things like movies, video games, or even virtual reality. |
Keywords
» Artificial intelligence » Autoencoder » Gan » Image generation » Machine learning
