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.
@article{arxiv.2508.00428,
title = {Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation},
author = {Nan Xiang and Tianyi Liang and Haiwen Huang and Shiqi Jiang and Hao Huang and Yifei Huang and Liangyu Chen and Changbo Wang and Chenhui Li},
journal= {arXiv preprint arXiv:2508.00428},
year = {2025}
}
Comments
IEEE VIS VAST 2025 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation methods