Abstract
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.
Abstract (translated)
我们介绍了Stylos，这是一个单一前向的3D高斯框架，用于在未布置的内容上进行三维风格转换，从单张图像到多视角集合，并以单独的参考风格图像为条件。Stylos 无需场景优化或预计算姿态即可合成具有几何感知和视图一致性的风格化3D高斯场景，从而能够泛化至未知类别、场景和风格。在核心方面，Stylos 采用了一个Transformer骨干网，并包含两个路径：几何预测保留自注意力以保持几何精度，而风格则通过全局跨注意力注入以确保视图间的一致性。随着基于体素的3D风格损失的加入，该损失将聚合场景特征与风格统计对齐，Stylos 在保持几何结构的同时实现了视图一致性的风格化。多数据集上的实验表明，Stylos 能够提供高质量的零样本风格化效果，这突显了全局风格-内容耦合的有效性、提出的3D风格损失以及我们的框架从单视角扩展到大规模多视角设置的能力。
URL
https://arxiv.org/abs/2509.26455
