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.
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.