CreativeFlow: A One-to-Many Analogical Relation Transfer Method for 3D Asset Generation
cs.AI, cs.GR
Submitted: 2026-10-04
Updated: 2026-10-04
Comments: 3 pages. To appear in SIGGRAPH Asia 2026 Posters (SA Posters '26), Kuala Lumpur, Malaysia, December 2026
Journal ref: SA '26 Posters: SIGGRAPH Asia 2026 Posters, Kuala Lumpur, Malaysia, 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
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.
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