HeadSculpt: Crafting 3D Head Avatars with Text
Authors: name: Xiao Han, name: Yukang Cao, name: Kai Han, name: Xiatian Zhu, name: Jiankang Deng, name: Yi-Zhe Song, name: Tao Xiang, name: Kwan-Yee K. Wong

Recently, text-guided 3D generative methods have made remarkable advancements
in producing high-quality textures and geometry, capitalizing on the
proliferation of large vision-language and image diffusion models. However,
existing methods still struggle to create high-fidelity 3D head avatars in two
aspects: (1) They rely mostly on a pre-trained text-to-image diffusion model
whilst missing the necessary 3D awareness and head priors. This makes them
prone to inconsistency and geometric distortions in the generated avatars. (2)
They fall short in fine-grained editing. This is primarily due to the inherited
limitations from the pre-trained 2D image diffusion models, which become more
pronounced when it comes to 3D head avatars. In this work, we address these
challenges by introducing a versatile coarse-to-fine pipeline dubbed HeadSculpt
for crafting (i.e., generating and editing) 3D head avatars from textual
prompts. Specifically, we first equip the diffusion model with 3D awareness by
leveraging landmark-based control and a learned textual embedding representing
the back view appearance of heads, enabling 3D-consistent head avatar
generations. We further propose a novel identity-aware editing score
distillation strategy to optimize a textured mesh with a high-resolution
differentiable rendering technique. This enables identity preservation while
following the editing instruction. We showcase HeadSculpt's superior fidelity
and editing capabilities through comprehensive experiments and comparisons with
existing methods.