Text-driven 3D scene generation techniques have made rapid progress in recent
years. Their success is mainly attributed to using existing generative models
to iteratively perform image warping and inpainting to generate 3D scenes.
However, these methods heavily rely on the outputs of existing models, leading
to error accumulation in geometry and appearance that prevent the models from
being used in various scenarios (e.g., outdoor and unreal scenarios). To
address this limitation, we generatively refine the newly generated local views
by querying and aggregating global 3D information, and then progressively
generate the 3D scene. Specifically, we employ a tri-plane features-based NeRF
as a unified representation of the 3D scene to constrain global 3D consistency,
and propose a generative refinement network to synthesize new contents with
higher quality by exploiting the natural image prior from 2D diffusion model as
well as the global 3D information of the current scene. Our extensive
experiments demonstrate that, in comparison to previous methods, our approach
supports wide variety of scene generation and arbitrary camera trajectories
with improved visual quality and 3D consistency.

利用文本驱动的 3D 场景生成技术，在构建 3D 场景时通过使用现有的生成模型进行图像变形和修复，同时结合查询和聚合全局 3D 信息来生成高质量的新内容，并在支持多种场景生成和任意相机路径的同时改善视觉效果和 3D 一致性。