Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D
  Generation
Authors: name: Nan Xiang, name: Tianyi Liang, name: Haiwen Huang, name: Shiqi Jiang, name: Hao Huang, name: Yifei Huang, name: Liangyu Chen, name: Changbo Wang, name: Chenhui Li

Text-to-3D (T23D) generation has transformed digital content creation, yet
remains bottlenecked by blind trial-and-error prompting processes that yield
unpredictable results. While visual prompt engineering has advanced in
text-to-image domains, its application to 3D generation presents unique
challenges requiring multi-view consistency evaluation and spatial
understanding. We present Sel3DCraft, a visual prompt engineering system for
T23D that transforms unstructured exploration into a guided visual process. Our
approach introduces three key innovations: a dual-branch structure combining
retrieval and generation for diverse candidate exploration; a multi-view hybrid
scoring approach that leverages MLLMs with innovative high-level metrics to
assess 3D models with human-expert consistency; and a prompt-driven visual
analytics suite that enables intuitive defect identification and refinement.
Extensive testing and user studies demonstrate that Sel3DCraft surpasses other
T23D systems in supporting creativity for designers.