用参考图快速生成风格一致的3D场景，解决颜色纹理不连贯问题。
ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization
- 分离内容与风格控制，防止信息泄露
- 通过邻视图注意力对齐提升局部一致性
- 适合影视游戏行业快速制作风格化3D场景
随着影视和游戏行业对目标风格3D场景的需求增长，先进3D风格化技术的重要性日益凸显。然而，现有方法常难以保持风格化场景中颜色与纹理的局部一致性，影响美学连贯性。为此，本文提出ArtNVG，一种基于3D高斯泼溅（3DGS）的创新3D风格化框架，可高效生成风格化3D场景。该框架通过两项关键技术实现高质量风格化：内容-风格分离控制与基于注意力的邻视图对齐。内容-风格分离控制采用CSGO模型与Tile ControlNet，解耦内容与风格调控，降低信息泄露风险；同时，基于注意力的邻视图对齐机制确保相邻视角间局部颜色与纹理的一致性，显著提升视觉质量。大量实验表明，ArtNVG在内容保留、风格对齐与局部一致性方面均优于现有方法。
原文摘要 · Abstract (English)
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.
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