Diffusion Model-Based Cartoon Style Transfer for Real-World 3D Scenes
Bibliographic Data
| ID | 22032964 |
|---|---|
| Authors | Yuhang Chen (0000-0002-4275-5458, China University of Geosciences), Haoran Zhou (0009-0000-1952-008X, Chinese Academy of Sciences, corresponding author), Jing Chen (0000-0002-7243-0806, China University of Geosciences), Nai Yang (0000-0001-5306-1163, China University of Geosciences), Jing Zhao (0009-0008-1906-5396, Nanjing Surveying and Mapping Research Institute (China)), Yi Chao (0000-0002-7286-5439, China University of Geosciences) |
| Year | 2025 |
| Volume | 14 |
| Issue | 8 |
| Pages | 303 |
| Publication date | 2025-08-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14080303 |
| OpenAlex | W4412941692 |
| Language | EN |
| Citations received | 1 |
| References cited | 23 |
Traditional map style transfer methods are mostly based on GAN, which are either overly artistic at the expense of conveying information, or insufficiently aesthetic by simply changing the color scheme of the map image. These methods often struggle to balance style transfer with semantic preservation and lack consistency in their transfer effects. In recent years, diffusion models have made significant progress in the field of image processing and have shown great potential in image-style transfer tasks. Inspired by these advances, this paper presents a method for transferring real-world 3D scenes to a cartoon style without the need for additional input condition guidance. The method combines pre-trained LDM with LoRA models to achieve stable and high-quality style infusion. By integrating DDIM Inversion, ControlNet, and MultiDiffusion strategies, it achieves the cartoon style transfer of real-world 3D scenes through initial noise control, detail redrawing, and global coordination. Qualitative and quantitative analyses, as well as user studies, indicate that our method effectively injects a cartoon style while preserving the semantic content of the real-world 3D scene, maintaining a high degree of consistency in style transfer. This paper offers a new perspective for map style transfer
Art · Diffusion · Physics · Visual arts · Advanced Vision and Imaging · Computer Science · Generative Adversarial Networks and Image Synthesis · Image Enhancement Techniques
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |