mlsanityData to 14 Oct 2026

GT2-GS: Geometry-aware Texture Transfer for Gaussian Splatting

arXiv 2505.15208 ↗First posted 21 May 2025cs.CVcited by 3

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Abstract (arXiv)

Transferring 2D textures onto complex 3D scenes plays a vital role in enhancing the efficiency and controllability of 3D multimedia content creation. However, existing 3D style transfer methods primarily focus on transferring abstract artistic styles to 3D scenes. 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 GT2-GS, a geometry-aware texture transfer framework for gaussian splatting. First, we propose a geometry-aware texture transfer loss that enables view-consistent texture transfer by leveraging prior view-dependent feature information and texture features augmented with additional geometric parameters. Moreover, an adaptive fine-grained control module is proposed to address the degradation of scene information caused by low-granularity texture features. Finally, a geometry preservation branch is introduced. This branch refines the geometric parameters using additionally bound Gaussian color priors, thereby decoupling the optimization objectives of appearance and geometry. 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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 47 references that are in the map. First posted on arXiv 21 May 2025.

Builds on

25 of 47 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  5. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  6. 2013Describing Textures in the Wildcited by 2,125
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  8. 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
  9. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  10. 2024StyleGaussian: Instant 3D Style Transfer with Gaussian Splattingcited by 79
  11. 2021Learning to Stylize Novel Viewscited by 76
  12. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
13 more
  1. 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
  2. 2024TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusioncited by 52
  3. 2022AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesiscited by 39
  4. 2022Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylizationcited by 35
  5. 2020Manifold Alignment for Semantically Aligned Style Transfercited by 34
  6. 2021SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfercited by 22
  7. 2024SGSST: Scaling Gaussian Splatting StyleTransfercited by 20
  8. 2024Geometry Transfer for Stylizing Radiance Fieldscited by 16
  9. 2024CoARF: Controllable 3D Artistic Style Transfer for Radiance Fieldscited by 14
  10. 2024StyleTex: Style Image-Guided Texture Generation for 3D Modelscited by 14
  11. 2021Texture Reformer: Towards Fast and Universal Interactive Texture Transfercited by 12
  12. 2023Single Mesh Diffusion Models with Field Latents for Texture Generationcited by 11
  13. 2024Dynamic Texture Transfer using PatchMatch and Transformerscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.