AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
[Sztuczna inteligencja] Autorzy: Xin Gao, Yifan Yang, Xin Li i 13 więcej
Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code models cannot see how their choices of layout and color come together on the canvas, often limiting spatial planning and aesthetic quality. Image generators excel at visual design but can misrender requested content, making their outputs unreliable targets for direct reproduction. We therefore introduce AesCode, which generates code grounded in the content prompt while using an image generated from the same prompt as an aesthetic guidance. To do so, AesCode represents the target as a design graph that describes the whole canvas and makes each property attributable. From this graph, we derive complementary cross-modal rewards: deterministic verifiers score properties parsable from code and rendering, while a VLM j…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja