Unlike the previous methods, e.g. DreamFusion [2], that
supervise the 3D generation with the semantic priors, we
leverage ﬁner-grained image priors inferred from the diffusion
model, which consequently allows our Text2NeRF to generate
more delicate geometric structure and realistic texture in
the 3D scenes. Speciﬁcally, we employ the diffusion model
to generate a text-related image as the content prior and
employ a monocular depth estimation method to offer the
geometric prior of the generated scene. Both content and
depth priors are leveraged to optimize the parameters of
the NeRF representation. Moreover, to guarantee consistency
between different views, we propose a progressive inpainting
and updating strategy (PIU) for the novel view synthesis of
the 3D scene. Through the PIU strategy, the generated scene
can be expanded and updated in a view-by-view manner
following a camera trajectory. In this way, the expanded area
of the current view can be reﬂected in the next view by
Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields