title: CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian
    Splatting
summary: Gaussian Splatting (GS) has recently emerged as an efficient representation
  for rendering 3D scenes from 2D images and has been extended to images, videos,
  and dynamic 4D content. However, applying style transfer to GS-based
  representations, especially beyond simple color changes, remains challenging.
  In this work, we introduce CLIPGaussians, the first unified style transfer
  framework that supports text- and image-guided stylization across multiple
  modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates
  directly on Gaussian primitives and integrates into existing GS pipelines as a
  plug-in module, without requiring large generative models or retraining from
  scratch. CLIPGaussians approach enables joint optimization of color and
  geometry in 3D and 4D settings, and achieves temporal coherence in videos,
  while preserving a model size. We demonstrate superior style fidelity and
  consistency across all tasks, validating CLIPGaussians as a universal and
  efficient solution for multimodal style transfer.
paper_authors._id: 6839877749c61862e2ca1ca6
paper_authors.name: Kornel Howil
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:45:31.792Z
paper_authors._id: 65fc24b804769daf21a98fa6
paper_authors.avatarUrl: /avatars/d7faf536940809823d56a442f55e9fb9.svg
paper_authors.fullname: Kornel
paper_authors.type: user
paper_authors.user: kornelhowil
paper_authors._id: 6839877749c61862e2ca1ca7
paper_authors.name: Joanna Waczyńska
paper_authors._id: 6839877749c61862e2ca1ca8
paper_authors.name: Piotr Borycki
paper_authors._id: 6839877749c61862e2ca1ca9
paper_authors.name: Tadeusz Dziarmaga
paper_authors._id: 6839877749c61862e2ca1caa
paper_authors.name: Marcin Mazur
paper_authors._id: 6839877749c61862e2ca1cab
paper_authors.name: Przemysław Spurek
paper_title: CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian
    Splatting
paper_summary: Gaussian Splatting (GS) has recently emerged as an efficient representation
  for rendering 3D scenes from 2D images and has been extended to images, videos,
  and dynamic 4D content. However, applying style transfer to GS-based
  representations, especially beyond simple color changes, remains challenging.
  In this work, we introduce CLIPGaussians, the first unified style transfer
  framework that supports text- and image-guided stylization across multiple
  modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates
  directly on Gaussian primitives and integrates into existing GS pipelines as a
  plug-in module, without requiring large generative models or retraining from
  scratch. CLIPGaussians approach enables joint optimization of color and
  geometry in 3D and 4D settings, and achieves temporal coherence in videos,
  while preserving a model size. We demonstrate superior style fidelity and
  consistency across all tasks, validating CLIPGaussians as a universal and
  efficient solution for multimodal style transfer.
paper_ai_summary: CLIPGaussians is a style transfer framework that supports text- and image-guided stylization of 2D images, videos, 3D objects, and 4D scenes by optimizing color and geometry directly on Gaussian primitives.
paper_ai_keywords: Gaussian Splatting CLIPGaussians style transfer Gaussian primitives 3D scenes 4D scenes joint optimization color and geometry temporal coherence