We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fields and camera poses. This latent representation is then used to learn a generative model that enables both unconditional and conditional generation of 3D scenes. Our model generalizes previous works that focus on single objects by removing the assumption that the camera pose distribution can be shared across samples. We show that GAUDI obtains state-of-the-art performance in the unconditional generative setting across multiple datasets and allows for conditional generation of 3D scenes given conditioning variables like sparse image observations or text that describes the scene.

GAUDI是一种生成模型，能够捕捉到复杂、逼真的三维场景的分布，并能够实时渲染出来。它以一种可扩展但强大的方式解决了这个具有挑战性的问题，首先优化了一个潜在表示，将辐射场和摄像机姿态进行了解耦。然后利用这个潜在表示来学习一个生成模型，使得它能够无条件和有条件地生成3D场景。GAUDI能够对多个数据集在无条件生成的情况下达到最先进的性能水平，并且可以在给定稀疏图像观测或文本描述场景等条件变量的情况下生成有条件的3D场景。

GAUDI：用于沉浸式 3D 场景生成的神经结构设计师