  Automatic 3D content creation has achieved rapid progress recently due to the
availability of pre-trained, large language models and image diffusion models,
forming the emerging topic of text-to-3D content creation. Existing text-to-3D
methods commonly use implicit scene representations, which couple the geometry
and appearance via volume rendering and are suboptimal in terms of recovering
finer geometries and achieving photorealistic rendering; consequently, they are
less effective for generating high-quality 3D assets. In this work, we propose
a new method of Fantasia3D for high-quality text-to-3D content creation. Key to
Fantasia3D is the disentangled modeling and learning of geometry and
appearance. For geometry learning, we rely on a hybrid scene representation,
and propose to encode surface normal extracted from the representation as the
input of the image diffusion model. For appearance modeling, we introduce the
spatially varying bidirectional reflectance distribution function (BRDF) into
the text-to-3D task, and learn the surface material for photorealistic
rendering of the generated surface. Our disentangled framework is more
compatible with popular graphics engines, supporting relighting, editing, and
physical simulation of the generated 3D assets. We conduct thorough experiments
that show the advantages of our method over existing ones under different
text-to-3D task settings. Project page and source codes:
https://fantasia3d.github.io/.
