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StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting

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arxiv 2403.07807 v1 pith:CWFMF6AJ submitted 2024-03-12 cs.CV

classification cs.CV
keywords featuresstylestylegaussiantransferinstantachievesconsistencymulti-view
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps). Leveraging 3D Gaussian Splatting (3DGS), StyleGaussian achieves style transfer without compromising its real-time rendering ability and multi-view consistency. It achieves instant style transfer with three steps: embedding, transfer, and decoding. Initially, 2D VGG scene features are embedded into reconstructed 3D Gaussians. Next, the embedded features are transformed according to a reference style image. Finally, the transformed features are decoded into the stylized RGB. StyleGaussian has two novel designs. The first is an efficient feature rendering strategy that first renders low-dimensional features and then maps them into high-dimensional features while embedding VGG features. It cuts the memory consumption significantly and enables 3DGS to render the high-dimensional memory-intensive features. The second is a K-nearest-neighbor-based 3D CNN. Working as the decoder for the stylized features, it eliminates the 2D CNN operations that compromise strict multi-view consistency. Extensive experiments show that StyleGaussian achieves instant 3D stylization with superior stylization quality while preserving real-time rendering and strict multi-view consistency. Project page: https://kunhao-liu.github.io/StyleGaussian/

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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. NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting

    cs.HC 2025-07 conditional novelty 6.0 of 10

    NLI4VolVis integrates multi-agent large language models, editable 3D Gaussian splatting, and CLIP-based querying so users can explore, query, and edit volume visualizations through natural language.

  3. Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Multi-StyleGS transfers multiple artistic styles to different semantic regions of a 3D Gaussian Splatting scene via bipartite matching and a semantic style loss.

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