  In this paper, we introduced a novel text-to-avatar generation method that
separately generates the human body and the clothes and allows high-quality
animation on the generated avatar. While recent advancements in text-to-avatar
generation have yielded diverse human avatars from text prompts, these methods
typically combine all elements-clothes, hair, and body-into a single 3D
representation. Such an entangled approach poses challenges for downstream
tasks like editing or animation. To overcome these limitations, we propose a
novel disentangled 3D avatar representation named Sequentially Offset-SMPL
(SO-SMPL), building upon the SMPL model. SO-SMPL represents the human body and
clothes with two separate meshes, but associates them with offsets to ensure
the physical alignment between the body and the clothes. Then, we design an
Score Distillation Sampling(SDS)-based distillation framework to generate the
proposed SO-SMPL representation from text prompts. In comparison with existing
text-to-avatar methods, our approach not only achieves higher exture and
geometry quality and better semantic alignment with text prompts, but also
significantly improves the visual quality of character animation, virtual
try-on, and avatar editing. Our project page is at
https://shanemankiw.github.io/SO-SMPL/.
