Agentic Image Generation
Overview
Agentic image generation studies systems that plan and act over multiple steps instead of producing an image from a prompt in one pass. Common approaches use vision-language models as planners or critics, coordinate specialized agents, or call tools for web search, image editing, code-based layouts, and workflow routing; some are training-free, while others use supervised fine-tuning and reinforcement learning. Work targets challenges such as object counts, spatial relationships, factual details, personalization, and editable designs, with evaluations including GenEval, WISE, and task-specific benchmarks. A central tension is how to make iterative decisions and verification reliable without adding excessive latency, cost, or unwanted changes to parts of an image that are already correct.
Latest papers
43 papers9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.