Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven
  Body Controllable Attribute
Chaoqun Gong Yuqin Dai Ronghui Li Achun Bao Jun Li Jian Yang Yachao Zhang Xiu Li
Generating 3D human models directly from text helps reduce the cost and time
of character modeling. However, achieving multi-attribute controllable and
realistic 3D human avatar generation is still challenging due to feature
coupling and the scarcity of realistic 3D human avatar datasets. To address
these issues, we propose Text2Avatar, which can generate realistic-style 3D
avatars based on the coupled text prompts. Text2Avatar leverages a discrete
codebook as an intermediate feature to establish a connection between text and
avatars, enabling the disentanglement of features. Furthermore, to alleviate
the scarcity of realistic style 3D human avatar data, we utilize a pre-trained
unconditional 3D human avatar generation model to obtain a large amount of 3D
avatar pseudo data, which allows Text2Avatar to achieve realistic style
generation. Experimental results demonstrate that our method can generate
realistic 3D avatars from coupled textual data, which is challenging for other
existing methods in this field.