Name
Abstract | ||
|---|---|---|
As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/COMPSAC54236.2022.00156 | 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022) |
Keywords | DocType | Citations |
Face Generation, Orthodontic Treatment, StyleGAN, Encoder-Decoder | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Liang Tian | 1 | 0 | 0.34 |
| Qin-Yan Zhang | 2 | 0 | 0.34 |
| Hai-Zhen Li | 3 | 0 | 0.34 |
| Qing Wang | 4 | 345 | 76.64 |
| Yi Lei | 5 | 0 | 0.68 |
| Lin Zang | 6 | 0 | 0.34 |
| Xuemei Gao | 7 | 0 | 0.34 |
| Ji-Jiang Yang | 8 | 232 | 35.53 |