Abstract
Transferring 2D textures to 3D modalities is of great significance for improving the efficiency of multimedia content creation. Existing approaches have rarely focused on transferring image textures onto 3D representations. 3D style transfer methods are capable of transferring abstract artistic styles to 3D scenes. However, these methods often overlook the geometric information of the scene, which makes it challenging to achieve high-quality 3D texture transfer results. In this paper, we present GT^2-GS, a geometry-aware texture transfer framework for gaussian splitting. From the perspective of matching texture features with geometric information in rendered views, we identify the issue of insufficient texture features and propose a geometry-aware texture augmentation module to expand the texture feature set. Moreover, a geometry-consistent texture loss is proposed to optimize texture features into the scene representation. This loss function incorporates both camera pose and 3D geometric information of the scene, enabling controllable texture-oriented appearance editing. Finally, a geometry preservation strategy is introduced. By alternating between the texture transfer and geometry correction stages over multiple iterations, this strategy achieves a balance between learning texture features and preserving geometric integrity. Extensive experiments demonstrate the effectiveness and controllability of our method. Through geometric awareness, our approach achieves texture transfer results that better align with human visual perception. Our homepage is available at this https URL.
Abstract (translated)
将2D纹理转移到3D模态对于提升多媒体内容创建的效率具有重要意义。现有方法很少关注如何将图像纹理映射到3D表示上。虽然现有的3D风格迁移方法能够把抽象的艺术风格转移到3D场景中，但这些方法常常忽略了场景的几何信息，这使得实现高质量的3D纹理转移变得困难。 在本文中，我们提出了一种基于高斯分割的、具有几何感知能力的纹理传输框架（GT^2-GS）。从匹配渲染视图中的纹理特征与几何信息的角度出发，我们发现了现有方法中存在的纹理特征不足的问题，并提出了一个增强几何感知的纹理扩展模块来扩充纹理特征集。此外，还提出了一种基于几何一致性的纹理损失函数，用以优化场景表示中的纹理特征。这一损失函数结合了相机姿态和3D场景的几何信息，支持对目标外观进行可控制的编辑。最后，我们引入了一个几何保持策略，在多个迭代中通过交替执行纹理转移与几何校正阶段来平衡学习到的纹理特征和保持几何完整性的关系。 广泛的实验结果证明了我们方法的有效性和可控性。通过增强对场景几何信息的理解，我们的方法实现了更好地符合人类视觉感知的纹理传输效果。 如需进一步了解，请访问我们的主页：[这里插入URL]
URL
https://arxiv.org/abs/2505.15208
