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April 23, 2026IEEE Transactions on Visualization and Computer Graphics0 citations

3D Gaussian Splatting Texture Editing via Single Modified Image

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HBHanul BaekDLDohae LeeKKKyumin Kim

Key Points

  • The aim is to develop a texture editing method for 3D Gaussian Splatting using a single image while maintaining coherence across views.
  • Developed a framework leveraging a single user-modified image for texture editing.
  • Implemented aligned edit propagation to transfer edits across viewpoints.
  • Used mask-based filtering and opacity-based selection for precise control over Gaussians.
  • Achieved more precise and spatially controllable 3D Gaussian Splatting editing compared to existing methods.
  • Demonstrated effectiveness through qualitative and quantitative evaluations on diverse datasets.

Abstract

Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for reconstructing high-quality, photorealistic 3D representations of real-world scenes. However, editing 3DGS remains more challenging than mesh-based approaches as it lacks explicit geometry and requires view-consistent updates across Gaussians. Previous approaches have relied primarily on text-driven generative models for 3D Gaussian editing, limiting direct control over specific visual appearances. In this study, we propose a texture editing framework for 3DGS that leverages only a single user-modified image. Our approach maintains coherence across multiple views while accounting for the corresponding lighting adjustments in the edited region. We introduce three key techniques to achieve this: (1) aligned edit propagation, which transfers local edits from the reference view to other viewpoints; (2) mask-based filtering, which restricts modifications to relevant Gaussians and prevents unintended changes; and (3) opacity-based selection, which identifies Gaussians with the most significant visual impact on the edited texture. Through both qualitative and quantitative evaluations on synthetic and real-world datasets, we demonstrate that our method achieves more precise and spatially controllable 3DGS editing than existing techniques. We expect these techniques to pave the way for more intuitive 3D Gaussian Splatting editing pipelines and inspire future research.

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Cite This Study

Baek et al. (2026) studied this question.

synapsesocial.com/papers/69e9b71b85696592c86eb1c4https://doi.org/10.1109/tvcg.2026.3684949
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