GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces
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.
6931d9752d1e5b0a7d84db8e
Melis Ocal
claimed_verified
2025-12-05T15:15:01.662Z
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Melis Ocal
melisocal
user
melisocal
6931d9752d1e5b0a7d84db8f
Xiaoyan Xing
6931d9752d1e5b0a7d84db90
Yue Li
6931d9752d1e5b0a7d84db91
Ngo Anh Vien
6931d9752d1e5b0a7d84db92
Sezer Karaoglu
6931d9752d1e5b0a7d84db93
Theo Gevers
GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces
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.
GaussianBlender, a feed-forward framework using latent diffusion models, enables instant, high-fidelity, and multi-view consistent 3D stylization through text-driven edits on disentangled latent spaces.
GaussianBlender feed-forward framework text-driven 3D stylization latent diffusion model structured disentangled latent spaces geometry-preserving multi-view consistent stylization