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InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting

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arxiv 2408.04249 v2 pith:TDUPVTRW submitted 2024-08-08 cs.CV

Xin-Yi Yu , Jun-Xin Yu , Li-Bo Zhou , Yan Wei , Lin-Lin Ou This is my paper · ORCID
classification cs.CV
keywords scenesstyledatasetmethodtransferdiffusiongaussianimages
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We present InstantStyleGaussian, an innovative 3D style transfer method based on the 3D Gaussian Splatting (3DGS) scene representation. By inputting a target-style image, it quickly generates new 3D GS scenes. Our method operates on pre-reconstructed GS scenes, combining diffusion models with an improved iterative dataset update strategy. It utilizes diffusion models to generate target style images, adds these new images to the training dataset, and uses this dataset to iteratively update and optimize the GS scenes, significantly accelerating the style editing process while ensuring the quality of the generated scenes. Extensive experimental results demonstrate that our method ensures high-quality stylized scenes while offering significant advantages in style transfer speed and consistency.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting

    cs.CV 2026-02 conditional novelty 6.0 of 10

    A single-pass 3D Gaussian splatting pipeline that stylizes unposed scenes from either a text prompt or a reference image via a lightweight zero-initialized style-injection branch.

  2. SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A diffusion-based pipeline with cross-view attention and instance-level group matching produces 3D style transfers with improved multi-view consistency on forward-facing and 360-degree scenes.

  3. OmniStyle-INR: Universal and Multimodal Style Transfer for INRs

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A single CLIP/VGG-guided fine-tuning recipe on per-modality implicit neural representations transfers style from text or images across 2D, video, 3D, and 4D, with optical-flow temporal regularization for dynamic scenes.

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