Abstract
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 a user study demonstrate that Sel3DCraft surpasses other T23D systems in supporting creativity for designers.
| Original language | English |
|---|---|
| Pages (from-to) | 331-341 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 32 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2026 |
Keywords
- Prompt engineering
- shape exploration
- text-to-3D generation
- visual perception
- visualization design
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