  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.
