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.

使用三维高斯喷洒方法进行场景风格化研究，通过在多视角环境中保持一致的风格外观来产生高质量的风格化新视角，实现了在增强现实或虚拟现实应用中具有卓越视觉质量的最新表现。

高斯飞溅式生成

Gaussian Splatting in Style

Controllable 3D indoor scene synthesis stands at the forefront of
technological progress, offering various applications like gaming, film, and
augmented/virtual reality. The capability to stylize and de-couple objects
within these scenarios is a crucial factor, providing an advanced level of
control throughout the editing process. This control extends not just to
manipulating geometric attributes like translation and scaling but also
includes managing appearances, such as stylization. Current methods for scene
stylization are limited to applying styles to the entire scene, without the
ability to separate and customize individual objects. Addressing the
intricacies of this challenge, we introduce a unique pipeline designed for
synthesis 3D indoor scenes. Our approach involves strategically placing objects
within the scene, utilizing information from professionally designed bounding
boxes. Significantly, our pipeline prioritizes maintaining style consistency
across multiple objects within the scene, ensuring a cohesive and visually
appealing result aligned with the desired aesthetic. The core strength of our
pipeline lies in its ability to generate 3D scenes that are not only visually
impressive but also exhibit features like photorealism, multi-view consistency,
and diversity. These scenes are crafted in response to various natural language
prompts, demonstrating the versatility and adaptability of our model.

我们提出了一个独特的流程，用于合成 3D 室内场景，该流程通过在场景中策略性地放置对象，并利用来自专业设计的边界框的信息来维持风格的一致性，生成不仅外观上令人印象深刻，而且具有照片般逼真、多视角一致性和多样性特征的 3D 场景，以适应各种自然语言提示。