Text2Avatar: Articulated 3D Avatar Creation With Text Instructions

Abstract

We propose a framework for creating articulated human avatars, editing their styles, and animating the human avatars from three different types of text instructions. The three types of instructions, identity, edit, and action, are fed into three models that generate, edit, and animate human avatars. Specifically, the proposed framework takes identity instruction and multi-view pose condition images to generate the images of a human using the avatar generation model. Then, the avatar can be edited with text instructions by changing the style of the images generated. We apply the Neural Radiance Field (NeRF) and Poisson reconstruction to extract a human mesh model from images and assign linear blend skinning (LBS) weights to the vertices. Finally, the action instructions can animate human avatars, where we use the off-the-shelf method to generate the motions from text instructions. Notably, our proposed method adapts the appearance of hundreds of different individuals to construct a conditionally editable avatar-generated model, allowing easy creation of 3D avatars using text instructions. We demonstrate high-fidelity 3D animatable avatar creation with text instructions on various datasets and highlight a superior performance of the proposed method compared to the previous studies.