Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents
This addresses the need for automated, high-quality ad banner design for commercial applications, representing an incremental improvement over existing generative models.
The paper tackles the problem of generating advertising banners that require structured layouts and precise branding by introducing MIMO, an agentic refinement framework, which significantly outperforms existing baselines in real-world scenarios.
Recent generative models such as GPT-4o have shown strong capabilities in producing high-quality images with accurate text rendering. However, commercial design tasks like advertising banners demand more than visual fidelity -- they require structured layouts, precise typography, consistent branding, and more. In this paper, we introduce MIMO (Mirror In-the-Model), an agentic refinement framework for automatic ad banner generation. MIMO combines a hierarchical multi-modal agent system (MIMO-Core) with a coordination loop (MIMO-Loop) that explores multiple stylistic directions and iteratively improves design quality. Requiring only a simple natural language based prompt and logo image as input, MIMO automatically detects and corrects multiple types of errors during generation. Experiments show that MIMO significantly outperforms existing diffusion and LLM-based baselines in real-world banner design scenarios.