Scene stylization extends the work of neural style transfer to three spatial dimensions. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across a multi-view setting. A vast majority of the previous works achieve this by optimizing the scene with a specific style image. In contrast, we propose a novel architecture trained on a collection of style images, that at test time produces high quality stylized novel views. Our work builds up on the framework of 3D Gaussian splatting. For a given scene, we take the pretrained Gaussians and process them using a multi resolution hash grid and a tiny MLP to obtain the conditional stylised views. The explicit nature of 3D Gaussians give us inherent advantages over NeRF-based methods including geometric consistency, along with having a fast training and rendering regime. This enables our method to be useful for vast practical use cases such as in augmented or virtual reality applications. Through our experiments, we show our methods achieve state-of-the-art performance with superior visual quality on various indoor and outdoor real-world data.
场景风格化将神经风格迁移的工作扩展到三维空间。在这个问题中，一个重要的挑战是在多视图设置中保持风格化外观的一致性。大多数以前的工作通过优化具有特定风格图片的场景来实现这一点。相比之下，我们提出了一种在风格图片集合上训练的新颖架构，这种架构在测试时可以产生高质量的风格化新视图。我们的工作是在3D高斯溅射框架上构建的。对于给定的场景，我们使用预训练的高斯体并通过多分辨率哈希网格和一个小型MLP（多层感知机）处理它们，以获得条件风格化视图。3D高斯的明确性质赋予我们相比基于NeRF（神经辐射场）的方法包括几何一致性在内的内在优势，同时拥有快速的训练和渲染体制。这使得我们的方法对于诸如增强现实或虚拟现实应用等广泛的实际用例非常有用。通过我们的实验，我们展示了我们的方法在各种室内外真实世界数据上达到了最先进的性能，并具有优越的视觉质量。