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. Our code is available at https://github.com/eckertzhang/Text2NeRF.

翻译：基于文本驱动的三维场景生成在电子游戏、影视工业及需要大量三维场景的元宇宙应用中具有广泛潜力。然而，现有文本到三维生成方法仅能生成几何结构简单、风格梦幻且缺乏真实感的三维物体。本文提出的Text2NeRF方法能够仅凭文本提示，生成具有复杂几何结构与高保真纹理的多样化三维场景。为此，我们采用NeRF作为三维表示，并利用预训练的文本到图像扩散模型约束NeRF的三维重建过程，使其准确反映场景描述。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，两者共同用于更新NeRF模型。为保障不同视角间的纹理与几何一致性，我们提出渐进式场景修复与更新策略，用于场景的新视角合成。本方法无需额外训练数据，仅需场景的自然语言描述作为输入即可完成生成。大量实验表明，Text2NeRF在从多样自然语言提示生成照片级真实、多视角一致且丰富多样的三维场景方面显著优于现有方法。我们的代码已开源至https://github.com/eckertzhang/Text2NeRF。