We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achieving geometry-aware, view-consistent stylization that generalizes to unseen categories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention to preserve geometric fidelity, while style is injected via global cross-attention to enforce visual consistency across views. With the addition of a voxel-based 3D style loss that aligns aggregated scene features to style statistics, Stylos enforces view-consistent stylization while preserving geometry. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of global style-content coupling, the proposed 3D style loss, and the scalability of our framework from single view to large-scale multi-view settings. Our codes are available at https://github.com/HanzhouLiu/Stylos.

翻译：本文提出Stylos，一种基于单次前向三维高斯框架的三维风格迁移方法，能够处理从单张图像到多视角集合的无位姿内容，并以独立的参考风格图像为条件。Stylos无需逐场景优化或预计算位姿即可合成风格化的三维高斯场景，实现了几何感知、视角一致的风格化，并能泛化至未见过的类别、场景及风格。其核心采用具有双分支的Transformer主干网络：几何预测分支保留自注意力机制以维持几何保真度，而风格则通过全局交叉注意力注入以确保跨视角的视觉一致性。结合提出的基于体素的三维风格损失——该损失通过聚合场景特征与风格统计量对齐，Stylos在保持几何结构的同时实现了视角一致的风格化。在多个数据集上的实验表明，Stylos能够实现高质量的零样本风格化，凸显了全局风格-内容耦合机制、所提出的三维风格损失以及本框架从单视角到大规模多视角设置的可扩展性的有效性。代码已开源：https://github.com/HanzhouLiu/Stylos。