无需优化即可快速实现大规模3D场景的逼真风格迁移。
FPGS: Feed-Forward Semantic-aware Photorealistic Style Transfer of Large-Scale Gaussian Splatting
- 引入风格解耦的3D特征场，支持任意风格参考图像的前馈式迁移。
- 在真实场景与动态场景上均实现高质量风格化，保持多视角一致性。
- 适合需要快速、灵活控制3D场景风格的创作者与开发者使用。
我们提出FPGS，一种针对由高斯点云表示的大规模辐射场的前馈式逼真风格迁移方法。FPGS可在不进行额外优化的情况下，对大规模3D场景应用任意多个风格参考图像，同时保持3D高斯的多视角一致性和实时渲染速度。以往方法需耗时的每风格优化或每场景训练，且仅限小规模场景。FPGS通过引入风格解耦的3D特征场，继承AdaIN的前馈式风格迁移机制，支持任意风格参考图像。此外，结合语义对应匹配与局部AdaIN，FPGS支持多参考风格迁移，增强用户对3D场景风格的多样化控制。通过直接在3D空间查询特征上应用语义匹配与风格迁移，有效保持多视角一致性。实验表明，FPGS在多样参考图像下，对大规模静态与动态3D场景均实现了出色的逼真风格化效果。
原文摘要 · Abstract (English)
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: https://kim-geonu.github.io/FPGS/
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