SYNTHIA: Novel Concept Design with Affordance Composition
Journal:
arXiv
Published Date:
Feb 25, 2025
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.
