SYNTHIA: Novel Concept Design with Affordance Composition

• URL: http://arxiv.org/abs/2502.17793v2
• Date: Thu, 10 Apr 2025 18:37:28 GMT
• Title: SYNTHIA: Novel Concept Design with Affordance Composition
• Authors: Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, Heng Ji,
• Abstract summary: We introduce SYNTHIA, a framework for generating novel, functionally coherent designs based on desired affordances.<n>We develop a curriculum learning scheme based on our ontology that contrast fine-tunes T2I models to progressively learn affordance composition.<n> Experimental results show that SYNTHIA outperforms state-of-the-art T2I models.
• Score: 114.19366716161655
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

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