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Recently, text-guided 3D generative methods have made remarkable advancementsin producing high-quality textures and geometry, capitalizing on theproliferation of large vision-language and image diffusion models. However,existing methods still struggle to create high-fidelity 3D head avatars in twoaspects: (1) They rely mostly on a pre-trained text-to-image diffusion modelwhilst missing the necessary 3D awareness and head priors. This makes themprone to inconsistency and geometric distortions in the generated avatars. (2)They fall short in fine-grained editing. This is primarily due to the inheritedlimitations from the pre-trained 2D image diffusion models, which become morepronounced when it comes to 3D head avatars. In this work, we address thesechallenges by introducing a versatile coarse-to-fine pipeline dubbed HeadSculptfor crafting (i.e., generating and editing) 3D head avatars from textualprompts. Specifically, we first equip the diffusion model with 3D awareness byleveraging landmark-based control and a learned textual embedding representingthe back view appearance of heads, enabling 3D-consistent head avatargenerations. We further propose a novel identity-aware editing scoredistillation strategy to optimize a textured mesh with a high-resolutiondifferentiable rendering technique. This enables identity preservation whilefollowing the editing instruction. We showcase HeadSculpt's superior fidelityand editing capabilities through comprehensive experiments and comparisons withexisting methods.

HeadSculpt: Crafting 3D Head Avatars with Text