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SYNTHIA: Novel Concept Design with Affordance Composition

Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, and Heng Ji, in ACL, 2025.

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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, functional coherence–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.

Bib Entry

@inproceedings{ha2025synthia,
  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},
  booktitle = {ACL},
  year = {2025}
}

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