@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://aclanthology.org/2025.acl-long.1020/",
doi = "10.18653/v1/2025.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."
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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, –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.</abstract>
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%0 Conference Proceedings
%T SYNTHIA: Novel Concept Design with Affordance Composition
%A Ha, Hyeonjeong
%A Jin, Xiaomeng
%A Kim, Jeonghwan
%A Liu, Jiateng
%A Wang, Zhenhailong
%A Nguyen, Khanh Duy
%A Blume, Ansel
%A Peng, Nanyun
%A Chang, Kai-Wei
%A Ji, Heng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ha-etal-2025-synthia
%X 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.
%R 10.18653/v1/2025.acl-long.1020
%U https://aclanthology.org/2025.acl-long.1020/
%U https://doi.org/10.18653/v1/2025.acl-long.1020
%P 20939-20958
Markdown (Informal)
[SYNTHIA: Novel Concept Design with Affordance Composition](https://aclanthology.org/2025.acl-long.1020/) (Ha et al., ACL 2025)
ACL
- Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, and Heng Ji. 2025. SYNTHIA: Novel Concept Design with Affordance Composition. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20939–20958, Vienna, Austria. Association for Computational Linguistics.