Anime Scene Generator from Real-world Scenario using Generative Adversarial Networks
| dc.contributor.advisor | Phan, Duy Hung | |
| dc.contributor.author | Le, Xuan Huy | |
| dc.contributor.author | Bui, Thi Bich Ngoc | |
| dc.date.accessioned | 2022-01-07T14:32:26Z | |
| dc.date.available | 2022-01-07T14:32:26Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | /handle/123456789/3230 | |
| dc.description.abstract | This thesis presents a unique approach for image cartoonization and style transferring: translating an image or video in real life into an aesthetic, anime-like frame. By paying exceptional attention to the animation painting conduct, we propose to separately distinguish three feature maps from pictures: the surface description that contains smooth color shading characteristic of animation pictures, the construction depiction that emulates flattened global content and clear boundaries in a typical anime frame, and the texture representation that reflects high-frequency surface, forms, and details in animation pictures. All the extracted information will be fed into the Generator with the help of a VGG based discriminator to learn how to cartoonize a real-world photo. The learning objectives of our technique are independently based on each extracted feature map, making our model controllable and adjustable. Our solution takes unpaired photos and cartoon/anime images for training which can be fine-tuned for different problems and art styles. It is also incredibly lightweight so as to provide quick and easy inference. Experimental results show that our method can generate high-quality cartoon images from real-world photos and outperforms many existing methods. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | FPTU Hà Nội | en_US |
| dc.subject | Computer Science | en_US |
| dc.subject | Style Transfer | en_US |
| dc.subject | Image Translation | en_US |
| dc.subject | Generative Adversarial Networks | en_US |
| dc.title | Anime Scene Generator from Real-world Scenario using Generative Adversarial Networks | en_US |
| dc.type | Thesis | en_US |
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