Sel3DCraft:为用户友好的文本到3D生成提供交互式视觉提示符
IEEE transactions on visualization and computer graphics
|December 2, 2025
概括
Sel3DCraft通过引导视觉提示工程来增强文本到3D (T23D) 的生成. 该系统通过一种新的评分方法和视觉分析改进了3D模型的创建,提高了设计师的创造力.
科学领域:
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 文本到3D (T23D) 生成正在推进数字内容创作.
- 目前的T23D方法依赖于试错提示,导致不可预测的结果.
- 用于3D的视觉提示符工程面临着在多视图一致性和空间理解方面的挑战.
研究的目的:
- 介绍Sel3DCraft,这是T23D的视觉提示工程系统.
- 将非结构化探索转化为为3D内容创作的指导视觉过程.
- 为了提高T23D世代的创造力和可预测性.
主要方法:
- 一个双分支结构,将检索和生成相结合,用于探索各种3D模型候选人.
- 一种多视图混合评分方法,使用多式大型语言模型 (MLLMs) 和高层次指标进行人与专家一致性评估.
- 一个提示驱动的视觉分析套件,用于直观的缺陷识别和3D模型的改进.
主要成果:
- 与现有的T23D系统相比,Sel3DCraft在支持设计师创造力方面表现出卓越的性能.
- 该系统有效地解决了多视图一致性和空间理解方面的挑战.
- 用户研究证实了该系统在指导式3D模型改进中的有效性.
结论:
- 塞尔3DCraft为T23D代提供了视觉提示工程的重大进步.
- 该系统为创建3D资产提供了更可控和直观的工作流.
- Sel3DCraft赋予设计师在数字内容创作方面增强创意能力.
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