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.

翻译：文本驱动的3D场景生成广泛应用于视频游戏、电影工业以及对3D场景有大量需求的元宇宙应用。然而，现有文本到3D的生成方法仅限于生成具有简单几何形状和梦幻风格且缺乏真实感的3D对象。本文提出Text2NeRF，该方法能够仅通过文本提示生成具有复杂几何结构和高保真纹理的多样化3D场景。为此，我们采用NeRF作为3D表示，并利用预训练的文本到图像扩散模型约束NeRF的3D重建过程，使其反映场景描述。具体而言，我们使用扩散模型推断与文本相关的图像作为内容先验，并采用单目深度估计方法提供几何先验。内容先验与几何先验均用于更新NeRF模型。为保证不同视角间纹理与几何的一致性，我们引入渐进式场景修复与更新策略，用于场景的新视角合成。该方法无需额外训练数据，仅需输入场景的自然语言描述。大量实验表明，我们的Text2NeRF在根据多种自然语言提示生成逼真、多视角一致且多样化的3D场景方面显著优于现有方法。