ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915182774583296 |
|---|---|
| author | Gu, Zixiao Li, Mengtian Chen, Ruhua Ji, Zhongxia Guo, Sichen Zhang, Zhenye Ye, Guangnan Hu, Zuo |
| author_facet | Gu, Zixiao Li, Mengtian Chen, Ruhua Ji, Zhongxia Guo, Sichen Zhang, Zhenye Ye, Guangnan Hu, Zuo |
| contents | As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coherence. To solve this problem, this paper introduces ArtNVG, an innovative 3D stylization framework that efficiently generates stylized 3D scenes by leveraging reference style images. Built on 3D Gaussian Splatting (3DGS), ArtNVG achieves rapid optimization and rendering while upholding high reconstruction quality. Our framework realizes high-quality 3D stylization by incorporating two pivotal techniques: Content-Style Separated Control and Attention-based Neighboring-View Alignment. Content-Style Separated Control uses the CSGO model and the Tile ControlNet to decouple the content and style control, reducing risks of information leakage. Concurrently, Attention-based Neighboring-View Alignment ensures consistency of local colors and textures across neighboring views, significantly improving visual quality. Extensive experiments validate that ArtNVG surpasses existing methods, delivering superior results in content preservation, style alignment, and local consistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization Gu, Zixiao Li, Mengtian Chen, Ruhua Ji, Zhongxia Guo, Sichen Zhang, Zhenye Ye, Guangnan Hu, Zuo Computer Vision and Pattern Recognition As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coherence. To solve this problem, this paper introduces ArtNVG, an innovative 3D stylization framework that efficiently generates stylized 3D scenes by leveraging reference style images. Built on 3D Gaussian Splatting (3DGS), ArtNVG achieves rapid optimization and rendering while upholding high reconstruction quality. Our framework realizes high-quality 3D stylization by incorporating two pivotal techniques: Content-Style Separated Control and Attention-based Neighboring-View Alignment. Content-Style Separated Control uses the CSGO model and the Tile ControlNet to decouple the content and style control, reducing risks of information leakage. Concurrently, Attention-based Neighboring-View Alignment ensures consistency of local colors and textures across neighboring views, significantly improving visual quality. Extensive experiments validate that ArtNVG surpasses existing methods, delivering superior results in content preservation, style alignment, and local consistency. |
| title | ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.18783 |