@inproceedings{ha-etal-2025-synthia,
    title = "{SYNTHIA}: Novel Concept Design with Affordance Composition",
    author = "Ha, Hyeonjeong  and
      Jin, Xiaomeng  and
      Kim, Jeonghwan  and
      Liu, Jiateng  and
      Wang, Zhenhailong  and
      Nguyen, Khanh Duy  and
      Blume, Ansel  and
      Peng, Nanyun  and
      Chang, Kai-Wei  and
      Ji, Heng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1020/",
    pages = "20939--20958",
    ISBN = "979-8-89176-251-0",
    abstract = "Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design. While recent studies focus on generating semantic and stylistic variations of given design concepts, {--}the integration of multiple affordances into a single coherent concept{--}remains largely overlooked. In this paper, we introduce SYNTHIA, a framework for generating novel, functionally coherent designs based on desired affordances. Our approach leverages a hierarchical concept ontology that decomposes concepts into parts and affordances, serving as a crucial building block for functionally coherent design. We also develop a curriculum learning scheme based on our ontology that contrastively fine-tunes T2I models to progressively learn affordance composition while maintaining visual novelty. To elaborate, we (i) gradually increase affordance distance, guiding models from basic concept-affordance association to complex affordance compositions that integrate parts of distinct affordances into a single, coherent form, and (ii) enforce visual novelty by employing contrastive objectives to push learned representations away from existing concepts. Experimental results show that SYNTHIA outperforms state-of-the-art T2I models, demonstrating absolute gains of 25.1{\%} and 14.7{\%} for novelty and functional coherence in human evaluation, respectively."
}