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Neural implicit fields are powerful for representing 3D scenes and generatinghigh-quality novel views, but it remains challenging to use such implicitrepresentations for creating a 3D human avatar with a specific identity andartistic style that can be easily animated. Our proposed method, AvatarCraft,addresses this challenge by using diffusion models to guide the learning ofgeometry and texture for a neural avatar based on a single text prompt. Wecarefully design the optimization framework of neural implicit fields,including a coarse-to-fine multi-bounding box training strategy, shaperegularization, and diffusion-based constraints, to produce high-qualitygeometry and texture. Additionally, we make the human avatar animatable bydeforming the neural implicit field with an explicit warping field that mapsthe target human mesh to a template human mesh, both represented usingparametric human models. This simplifies animation and reshaping of thegenerated avatar by controlling pose and shape parameters. Extensiveexperiments on various text descriptions show that AvatarCraft is effective androbust in creating human avatars and rendering novel views, poses, and shapes.Our project page is: https://avatar-craft.github.io/.

AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control