Advances in text-guided 3D editing: a survey

Abstract

In 3D Artificial Intelligence Generated Content (AIGC), compared with generating 3D assets from scratch, editing extant 3D assets satisfies user prompts, allowing the creation of diverse and high-quality 3D assets in a time and labor-saving manner. More recently, text-guided 3D editing that modifies 3D assets guided by text prompts is user-friendly and practical, which evokes a surge in research within this field. In this survey, we comprehensively investigate recent literature on text-guided 3D editing in an attempt to answer two questions: What are the methodologies of existing text-guided 3D editing? How has current progress in text-guided 3D editing gone so far? Specifically, we focus on text-guided 3D editing methods published in the past 4 years, delving deeply into their frameworks and principles. We then present a fundamental taxonomy in terms of the editing strategy, optimization scheme, and 3D representation. Based on the taxonomy, we review recent advances in this field, considering factors such as editing scale, type, granularity, and perspective. In addition, we highlight four applications of text-guided 3D editing, including texturing, style transfer, local editing of scenes, and insertion editing, to exploit further the 3D editing capacities with in-depth comparisons and discussions. Depending on the insights achieved by this survey, we discuss open challenges and future research directions. We hope this survey will help readers gain a deeper understanding of this exciting field and foster further advancements in text-guided 3D editing.