CreativeFlow: A One-to-Many Analogical Relation Transfer Method for 3D Asset Generation

Abstract: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.

3 pages. To appear in SIGGRAPH Asia 2026 Posters (SA Posters '26), Kuala Lumpur, Malaysia, December 2026

Artificial Intelligence (cs.AI); Graphics (cs.GR)

arXiv:2610.05167 [cs.AI]

(or arXiv:2610.05167v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.05167 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3829333.3847960 Focus to learn more DOI(s) linking to related resources

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