Text-to-3D generation has achieved remarkable success via large-scale
text-to-image diffusion models. Nevertheless, there is no paradigm for scaling
up the methodology to urban scale. Urban scenes, characterized by numerous
elements, intricate arrangement relationships, and vast scale, present a
formidable barrier to the interpretability of ambiguous textual descriptions
for effective model optimization. In this work, we surmount the limitations by
introducing a compositional 3D layout representation into text-to-3D paradigm,
serving as an additional prior. It comprises a set of semantic primitives with
simple geometric structures and explicit arrangement relationships,
complementing textual descriptions and enabling steerable generation. Upon
this, we propose two modifications -- (1) We introduce Layout-Guided
Variational Score Distillation to address model optimization inadequacies. It
conditions the score distillation sampling process with geometric and semantic
constraints of 3D layouts. (2) To handle the unbounded nature of urban scenes,
we represent 3D scene with a Scalable Hash Grid structure, incrementally
adapting to the growing scale of urban scenes. Extensive experiments
substantiate the capability of our framework to scale text-to-3D generation to
large-scale urban scenes that cover over 1000m driving distance for the first
time. We also present various scene editing demonstrations, showing the powers
of steerable urban scene generation. Website: this https URL

通过引入一种组合式 3D 布局表示方法到文本转 3D 范式中的本文，克服了在大规模城市范围内扩展这一方法的局限性，并且成功地将文本转 3D 技术应用于覆盖超过 1000m 行驶距离的大尺度城市场景，同时展示了可调控城市场景生成的强大能力。