Name
Abstract | ||
|---|---|---|
Text effects transfer can dramatically make the text visually pleasing. In this paper, we present a novel framework to stylize the text with exquisite decor, which are ignored by the previous text stylization methods. Decorative elements pose a challenge to spontaneously handle basal text effects and decor, which are two different styles. To address this issue, our key idea is to learn to separate, transfer and recombine the decors and the basal text effect. A novel text effect transfer network is proposed to infer the styled version of the target text. The stylized text is finally embellished with decor where the placement of the decor is carefully determined by a novel structure-aware strategy. Furthermore, we propose a domain adaptation strategy for decor detection and a one-shot training strategy for text effects transfer, which greatly enhance the robustness of our network to new styles. We base our experiments on our collected topography dataset including 59,000 professionally styled text and demonstrate the superiority of our method over other state-of-the-art style transfer methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00604 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | ISSN |
Typography,Computer vision,Computer science,Human–computer interaction,Artificial intelligence | Conference | 1063-6919 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjing Wang | 1 | 9 | 3.95 |
| Jiaying Liu | 2 | 860 | 83.96 |
| Shuai Yang | 3 | 41 | 5.79 |
| Zongming Guo | 4 | 778 | 81.98 |