用3D高斯点云实现可控风格迁移，更忠实还原参考图整体风格。
ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting
- 通过分割掩码实现内容与风格特征的精准对齐。
- 提出基于特征对齐的风格损失函数，提升全局风格一致性。
- 保留原始几何信息，支持精细风格控制，适合艺术化场景生成。
基于神经辐射场（NeRF）的3D场景风格化方法通过最近邻特征匹配（NNFM）损失取得了良好效果，但该损失未考虑全局风格信息，且NeRF的隐式表示限制了对结果场景的细粒度控制。本文提出ABC-GS框架，基于3D高斯点云实现高质量3D风格迁移。设计可控制的匹配阶段，通过分割掩码精确对齐场景内容与风格特征；提出基于特征对齐的风格迁移损失函数，确保风格迁移结果准确反映参考图像的全局风格；同时利用深度损失和高斯正则项保留原始几何信息。大量实验表明，ABC-GS具备风格迁移的可控性，生成结果更忠实于所选艺术参考图的整体风格。
原文摘要 · Abstract (English)
3D scene stylization approaches based on Neural Radiance Fields (NeRF) achieve promising results by optimizing with Nearest Neighbor Feature Matching (NNFM) loss. However, NNFM loss does not consider global style information. In addition, the implicit representation of NeRF limits their fine-grained control over the resulting scenes. In this paper, we introduce ABC-GS, a novel framework based on 3D Gaussian Splatting to achieve high-quality 3D style transfer. To this end, a controllable matching stage is designed to achieve precise alignment between scene content and style features through segmentation masks. Moreover, a style transfer loss function based on feature alignment is proposed to ensure that the outcomes of style transfer accurately reflect the global style of the reference image. Furthermore, the original geometric information of the scene is preserved with the depth loss and Gaussian regularization terms. Extensive experiments show that our ABC-GS provides controllability of style transfer and achieves stylization results that are more faithfully aligned with the global style of the chosen artistic reference. Our homepage is available at https://vpx-ecnu.github.io/ABC-GS-website.
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