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StyleSplat: 3D Object Style Transfer with Gaussian Splatting

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arxiv 2407.09473 v1 pith:HLUUCHYT submitted 2024-07-12 cs.CV

classification cs.CV
keywords styleobjectstransferscenesstylesplatassetsgaussiangaussians
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Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing techniques are often slow or unable to localize style transfer to specific objects. We introduce StyleSplat, a lightweight method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. Our approach first learns a photorealistic representation of the scene using 3D Gaussian splatting while jointly segmenting individual 3D objects. We then use a nearest-neighbor feature matching loss to finetune the Gaussians of the selected objects, aligning their spherical harmonic coefficients with the style image to ensure consistency and visual appeal. StyleSplat allows for quick, customizable style transfer and localized stylization of multiple objects within a scene, each with a different style. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.

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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. TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting

    cs.GR 2025-07 conditional novelty 6.0 of 10

    A textured Gaussian splatting framework enables flexible image- and text-driven style editing of volume visualizations with real-time rendering.

  2. Mastering Regional 3DGS: Locating, Initializing, and Editing with Diverse 2D Priors

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A 3D Gaussian Splatting editing pipeline that combines 2D diffusion localization, depth-based point seeding, and sequential view refinement to achieve up to 4x faster local edits.

  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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