ArtNVG：内容-风格分离的艺术邻视图高斯风格化
ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization
AI总结：
针对3D风格化中局部颜色纹理不一致的问题，提出基于3DGS的ArtNVG框架，通过内容-风格分离控制与注意力邻视图对齐，实现高质量风格化并超越现有方法。
摘要：
随着电影和游戏行业对具有目标风格的3D场景的需求不断增长，先进3D风格化技术的重要性日益凸显。然而，近期方法往往难以在风格化场景中保持颜色和纹理的局部一致性，而这种一致性对于维持美学连贯性至关重要。为解决这一问题，本文提出了ArtNVG，一种创新的3D风格化框架，通过利用参考风格图像高效生成风格化的3D场景。基于3D高斯泼溅（3DGS），ArtNVG在保持高重建质量的同时实现了快速优化和渲染。我们的框架通过引入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视图对齐。内容-风格分离控制使用CSGO模型和Tile ControlNet来解耦内容与风格控制，降低信息泄漏的风险。同时，基于注意力的邻视图对齐确保了相邻视图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超越了现有方法，在内容保留、风格对齐和局部一致性方面均取得了更优的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
