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Generating 3D human models directly from text helps reduce the cost and timeof character modeling. However, achieving multi-attribute controllable andrealistic 3D human avatar generation is still challenging due to featurecoupling and the scarcity of realistic 3D human avatar datasets. To addressthese issues, we propose Text2Avatar, which can generate realistic-style 3Davatars based on the coupled text prompts. Text2Avatar leverages a discretecodebook as an intermediate feature to establish a connection between text andavatars, enabling the disentanglement of features. Furthermore, to alleviatethe scarcity of realistic style 3D human avatar data, we utilize a pre-trainedunconditional 3D human avatar generation model to obtain a large amount of 3Davatar pseudo data, which allows Text2Avatar to achieve realistic stylegeneration. Experimental results demonstrate that our method can generaterealistic 3D avatars from coupled textual data, which is challenging for otherexisting methods in this field.

Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute