Text-driven 3D scene generation is widely applicable to video gaming, film
industry, and metaverse applications that have a large demand for 3D scenes.
However, existing text-to-3D generation methods are limited to producing 3D
objects with simple geometries and dreamlike styles that lack realism. In this
work, we present Text2NeRF, which is able to generate a wide range of 3D scenes
with complicated geometric structures and high-fidelity textures purely from a
text prompt. To this end, we adopt NeRF as the 3D representation and leverage a
pre-trained text-to-image diffusion model to constrain the 3D reconstruction of
the NeRF to reflect the scene description. Specifically, we employ the
diffusion model to infer the text-related image as the content prior and use a
monocular depth estimation method to offer the geometric prior. Both content
and geometric priors are utilized to update the NeRF model. To guarantee
textured and geometric consistency between different views, we introduce a
progressive scene inpainting and updating strategy for novel view synthesis of
the scene. Our method requires no additional training data but only a natural
language description of the scene as the input. Extensive experiments
demonstrate that our Text2NeRF outperforms existing methods in producing
photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of
natural language prompts.

Text2NeRF 是一种基于 NeRF 模型以自然语言描述为输入的 3D 场景生成方法，利用预训练的文本到图像扩散模型和单目深度估计方法约束 NeRF 模型以保持内容和几何一致，同时使用逐步场景修复和更新策略保证场景视角一致性。实验结果表明该方法能够生成高保真度，多视角一致性和多样性的真实感 3D 场景。