GT^2-GS: Geometry-aware Texture Transfer for Gaussian Splatting

• URL: http://arxiv.org/abs/2505.15208v1
• Date: Wed, 21 May 2025 07:37:29 GMT
• Title: GT^2-GS: Geometry-aware Texture Transfer for Gaussian Splatting
• Authors: Wenjie Liu, Zhongliang Liu, Junwei Shu, Changbo Wang, Yang Li,
• Abstract summary: GT2-GS is a geometry-aware texture transfer framework for gaussian splitting.<n>A geometry-consistent texture loss is proposed to optimize texture features into the scene representation.<n>Our approach achieves texture transfer results that better align with human visual perception.
• Score: 10.688496236943182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://vpx-ecnu.github.io/GT2-GS-website.

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