Stylos：基于单次前向高斯点云的多视图3D风格迁移
Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting
AI总结：
Stylos通过单次前向高斯点云框架实现多视图3D风格迁移，采用Transformer主干和全局交叉注意力机制，结合体素损失实现视图一致的风格化效果。
摘要：
我们提出了Stylos，一种单次前向3D高斯框架，用于3D风格迁移，其操作对象是未指定内容，从单张图像到多视图集合，基于单独的参考风格图像进行条件生成。Stylos合成一个风格化的3D高斯场景，无需每场景优化或预计算姿态，实现了具有几何感知、视图一致的风格迁移，能够泛化到未见过的类别、场景和风格。其核心采用Transformer主干，包含两条路径：几何预测保留自注意力以保持几何保真度，而风格通过全局交叉注意力注入以在不同视图间保持视觉一致性。此外，通过添加基于体素的3D风格损失，将聚合场景特征对齐到风格统计，Stylos在保持几何的前提下实现了视图一致的风格迁移。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迁移，突显了全局风格-内容耦合、所提3D风格损失以及框架从单视图到大规模多视图设置的可扩展性。我们的代码可在https://github.com/HanzhouLiu/Stylos获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
