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.
随着电影和游戏产业对具有目标风格的3D场景需求的增长，先进的3D风格化技术的重要性也在提升。然而，近期的方法常常难以在整个风格化场景中保持颜色和纹理的局部一致性，而这对于维持美学连贯性至关重要。为了解决这一问题，本文介绍了ArtNVG，这是一种创新的3D风格化框架，通过利用参考风格图像高效地生成风格化的3D场景。基于3D高斯散射（3DGS），ArtNVG在保持高重建质量的同时，实现了快速优化和渲染。我们的框架通过结合两项关键技术实现了高质量的3D风格化：内容-风格分离控制和基于注意力的邻近视图对齐。内容-风格分离控制使用CSGO模型和Tile ControlNet来分离内容和风格控制，降低信息泄露的风险。同时，基于注意力的邻近视图对齐确保了邻近视图之间局部颜色和纹理的一致性，显著提升了视觉质量。大量实验验证了ArtNVG优于现有方法，在内容保留、风格对齐和局部一致性方面提供了更为出色的结果。