GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces

• URL: http://arxiv.org/abs/2512.03683v1
• Date: Wed, 03 Dec 2025 11:23:07 GMT
• Title: GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces
• Authors: Melis Ocal, Xiaoyan Xing, Yue Li, Ngo Anh Vien, Sezer Karaoglu, Theo Gevers,
• Abstract summary: 3D stylization is central to game development, virtual reality, and digital arts.<n>Existing text-to-3D stylization methods distill from 2D image editors.<n>We introduce a pioneering feed-forward framework for text-driven 3D stylization that performs edits instantly at inference.
• Score: 26.8406399975604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods that support fast, high-fidelity manipulation. Existing text-to-3D stylization methods typically distill from 2D image editors, requiring time-intensive per-asset optimization and exhibiting multi-view inconsistency due to the limitations of current text-to-image models, which makes them impractical for large-scale production. In this paper, we introduce GaussianBlender, a pioneering feed-forward framework for text-driven 3D stylization that performs edits instantly at inference. Our method learns structured, disentangled latent spaces with controlled information sharing for geometry and appearance from spatially-grouped 3D Gaussians. A latent diffusion model then applies text-conditioned edits on these learned representations. Comprehensive evaluations show that GaussianBlender not only delivers instant, high-fidelity, geometry-preserving, multi-view consistent stylization, but also surpasses methods that require per-instance test-time optimization - unlocking practical, democratized 3D stylization at scale.

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