Sel3DCraft：用戶友好的文本到3D生成之互動視覺提示

摘要

文本到3D（T23D）生成技術已革新了數字內容創作，但仍受制於盲目的試錯提示過程，導致結果難以預測。儘管視覺提示工程在文本到圖像領域已取得進展，但其在3D生成中的應用面臨獨特挑戰，需要多視角一致性評估和空間理解。我們提出了Sel3DCraft，這是一個專為T23D設計的視覺提示工程系統，將無結構的探索轉化為有指導的視覺過程。我們的方法引入了三大創新：結合檢索與生成的雙分支結構，用於多樣化候選探索；多視角混合評分方法，利用多模態大語言模型（MLLMs）及創新高層次指標，以人類專家一致性評估3D模型；以及提示驅動的視覺分析套件，支持直觀的缺陷識別與精細化。廣泛的測試與用戶研究表明，Sel3DCraft在支持設計師創造力方面超越了其他T23D系統。

English

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.

Sel3DCraft：用戶友好的文本到3D生成之互動視覺提示

Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation

摘要

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