通过部件重组实现可控3D生成，减少反复试错
Compos3D: Interactive Part-Based Composition for Creative Control in Generative 3D Models
- 用户可从多个生成结果中选取2D区域或3D片段进行组合
- 重构后的模型在保持设计意图的同时修复几何问题
- 实验证明该方法比重复生成更符合创作意图
尽管生成式AI为3D内容创作带来新可能，但现有流程常依赖多次重生成，控制力弱且结果不可预测。我们提出Compos3D系统，引入基于部件重组的生成式3D建模工作流。用户先通过文本或图像提示生成多个候选模型，再通过2D图像区域或3D网格片段选择感兴趣部分，组装成连贯设计。系统将这些组件合成最终3D模型，既保留高层设计意图，又解决底层几何问题。我们通过受控用户研究，在2D与3D模态下对比了重组与重生成两种流程。结果显示，重组工作流显著提升用户创作控制力、意图对齐度与满意度。最后给出未来AI辅助3D建模流程的设计建议。
原文摘要 · Abstract (English)
While generative AI has unlocked new opportunities for 3D content creation, current workflows often rely on multiple regenerations, which provides limited control and unpredictable outcomes. We present Compos3D, a system that introduces a compositional workflow for generative 3D modeling through remixing. Instead of repeatedly regenerating models, users generate multiple candidates from text or image prompts, select parts of interest via 2D image regions or 3D mesh segments, and assemble them into a coherent design. The system synthesizes these compositions into a refined 3D model, preserving high-level intent while resolving low-level geometry. To evaluate this approach, we conducted a controlled user study comparing remixing and regeneration workflows across both 2D and 3D modalities. Results show that the remixing workflow provides participants with greater creative control, stronger alignment with their intent, and higher satisfaction. We conclude with design recommendations for future AI-assisted 3D modeling workflows.
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