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：利用解耦潜在空间实现3D高斯模型的即时风格化 / GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces
这篇论文提出了一种名为GaussianBlender的新方法，它能够根据文字描述，在无需针对每个3D模型进行耗时优化的前提下，快速、高质量地改变3D物体的视觉风格，同时保持其原有形状和多视角一致性，为游戏和虚拟现实等领域的大规模3D内容创作提供了实用工具。