Kai-Wei Chang, Khanh Nguyen, Nanyun Peng, Jeonghwan Kim, Heng Ji, Hyeonjeong Ha, Zhenhailong Wang, Ansel Blume, Jiateng Liu, Jin Xiaomeng
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| HyeonjeongHa/SYNTHIA | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| affordance_sampling | Not yet run | HyeonjeongHa/SYNTHIA/gen_curriculum.py pointer only (licence: NONE) · get_code("abebfee3ca629923") |
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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 visually novel and 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. Code is available at https://github.com/HyeonjeongHa/SYNTHIA.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.17793")
get_code_for_paper("2502.17793")
have("2502.17793")
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