  We present HeadEvolver, a novel framework to generate stylized head avatars
from text guidance. HeadEvolver uses locally learnable mesh deformation from a
template head mesh, producing high-quality digital assets for detail-preserving
editing and animation. To tackle the challenges of lacking fine-grained and
semantic-aware local shape control in global deformation through Jacobians, we
introduce a trainable parameter as a weighting factor for the Jacobian at each
triangle to adaptively change local shapes while maintaining global
correspondences and facial features. Moreover, to ensure the coherence of the
resulting shape and appearance from different viewpoints, we use pretrained
image diffusion models for differentiable rendering with regularization terms
to refine the deformation under text guidance. Extensive experiments
demonstrate that our method can generate diverse head avatars with an
articulated mesh that can be edited seamlessly in 3D graphics software,
facilitating downstream applications such as more efficient animation with
inherited blend shapes and semantic consistency.
