  The ability to generate diverse 3D articulated head avatars is vital to a
plethora of applications, including augmented reality, cinematography, and
education. Recent work on text-guided 3D object generation has shown great
promise in addressing these needs. These methods directly leverage pre-trained
2D text-to-image diffusion models to generate 3D-multi-view-consistent radiance
fields of generic objects. However, due to the lack of geometry and texture
priors, these methods have limited control over the generated 3D objects,
making it difficult to operate inside a specific domain, e.g., human heads. In
this work, we develop a new approach to text-guided 3D head avatar generation
to address this limitation. Our framework directly operates on the geometry and
texture of an articulable 3D morphable model (3DMM) of a head, and introduces
novel optimization procedures to update the geometry and texture while keeping
the 2D and 3D facial features aligned. The result is a 3D head avatar that is
consistent with the text description and can be readily articulated using the
deformation model of the 3DMM. We show that our diffusion-based articulated
head avatars outperform state-of-the-art approaches for this task. The latter
are typically based on CLIP, which is known to provide limited diversity of
generation and accuracy for 3D object generation.
