发表机构
College of Design and Innovation, Tongji University; Adobe Research（同济大学设计创意学院; Adobe研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
受认知科学启发，提出CREATIVEFLOW框架，通过显式建模类比发散思维，生成关系相似但几何不同的资产对，显著提升3D资产生成的创意新颖性，并建立基础数据集与基准。
AI 中文摘要
受认知科学启发，我们提出了CREATIVEFLOW，一个类比生成框架，该框架显式建模类比发散思维，以缓解文本到3D流程中的创意同质化问题。我们的方法推导出一系列有意义且关系相似的源-目标资产对，每对具有不同的几何配置。专家评估表明，我们的框架显著增强了创意新颖性和视觉吸引力。该工作流及其生成的资产为未来关系感知的3D模型训练建立了基础数据集和基准。
英文摘要
Inspired by cognitive science, we present CREATIVEFLOW, an analogical generation framework that explicitly models analogical divergent thinking to mitigate creative homogenization in text-to-3D pipelines. Our method derives a series of meaningful yet relationally similar source-target asset pairs, each featuring distinct geometric configurations. Expert evaluations demonstrate that our framework substantially enhances creative novelty and visual fascination. This workflow and its resulting assets establish a foundational dataset and benchmark for future relation-aware 3D model training.
Comments3 pages. To appear in SIGGRAPH Asia 2026 Posters (SA Posters '26), Kuala Lumpur, Malaysia, December 2026
Journal refSA '26 Posters: SIGGRAPH Asia 2026 Posters, Kuala Lumpur, Malaysia, 2026
