Personalize Your Gaussian: Consistent 3D Scene Personalization from a Single Image

• URL: http://arxiv.org/abs/2505.14537v2
• Date: Tue, 05 Aug 2025 09:48:08 GMT
• Title: Personalize Your Gaussian: Consistent 3D Scene Personalization from a Single Image
• Authors: Yuxuan Wang, Xuanyu Yi, Qingshan Xu, Yuan Zhou, Long Chen, Hanwang Zhang,
• Abstract summary: We present Consistent Personalization for 3D Gaussian Splatting (CP-GS), a framework that propagates the single-view reference appearance to novel perspectives.<n>In particular, CP-GS integrates pre-trained image-to-3D generation and iterative LoRA fine-tuning to extract and extend the reference appearance.
• Score: 56.134832639494185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalizing 3D scenes from a single reference image enables intuitive user-guided editing, which requires achieving both multi-view consistency across perspectives and referential consistency with the input image. However, these goals are particularly challenging due to the viewpoint bias caused by the limited perspective provided in a single image. Lacking the mechanisms to effectively expand reference information beyond the original view, existing methods of image-conditioned 3DGS personalization often suffer from this viewpoint bias and struggle to produce consistent results. Therefore, in this paper, we present Consistent Personalization for 3D Gaussian Splatting (CP-GS), a framework that progressively propagates the single-view reference appearance to novel perspectives. In particular, CP-GS integrates pre-trained image-to-3D generation and iterative LoRA fine-tuning to extract and extend the reference appearance, and finally produces faithful multi-view guidance images and the personalized 3DGS outputs through a view-consistent generation process guided by geometric cues. Extensive experiments on real-world scenes show that our CP-GS effectively mitigates the viewpoint bias, achieving high-quality personalization that significantly outperforms existing methods. The code will be released at https://github.com/Yuxuan-W/CP-GS.

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