Abstract
We present FPGS, a feed-forward photorealistic style transfer method of large-scale radiance fields represented by Gaussian Splatting. FPGS, stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization while preserving multi-view consistency and real-time rendering speed of 3D Gaussians. Prior arts required tedious per-style optimization or time-consuming per-scene training stage and were limited to small-scale 3D scenes. FPGS efficiently stylizes large-scale 3D scenes by introducing a style-decomposed 3D feature field, which inherits AdaIN's feed-forward stylization machinery, supporting arbitrary style reference images. Furthermore, FPGS supports multi-reference stylization with the semantic correspondence matching and local AdaIN, which adds diverse user control for 3D scene styles. FPGS also preserves multi-view consistency by applying semantic matching and style transfer processes directly onto queried features in 3D space. In experiments, we demonstrate that FPGS achieves favorable photorealistic quality scene stylization for large-scale static and dynamic 3D scenes with diverse reference images. Project page: this https URL
Abstract (translated)
我们介绍了一种名为FPGS的前馈式逼真风格转移方法，该方法用于通过高斯点表示的大规模辐射场。FPGS能够利用任意多个风格参考图像对大规模三维场景进行风格化处理，而无需额外优化，并且在保持多视角一致性和实时渲染速度的同时，保留了3D高斯分布的特性。相比之下，先前的技术需要繁琐的每种风格单独优化或耗时的每个场景训练阶段，并且仅限于小规模的三维场景。 FPGS通过引入一种风格分解的三维特征场，高效地对大规模三维场景进行样式化处理，这种方法继承了AdaIN（可适应实例归一化）的前馈式风格化机制，支持任意风格参考图像。此外，FPGS还通过语义匹配和局部AdaIN实现了多参考风格化，为三维场景提供了多样化的用户控制选项。 为了保持多视角一致性，FPGS直接在查询到的3D空间中的特征上应用语义匹配和样式传输过程。实验表明，FPGS能够对大规模静态和动态三维场景使用各种参考图像实现高质量、逼真的场景风格化处理。项目页面: [此链接](this https URL)
URL
https://arxiv.org/abs/2503.09635
