Summary
This paper, 'From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing' by Anirudh Sundara Rajan, Krishna Kumar Singh, and Yong Jae Lee (arXiv:2605.15181), addresses the weakness of modern image editing models with abstract, multi-step instructions such as 'make this advertisement more vegetarian-friendly.' Prior agent-based methods decompose tasks but rely on handcrafted pipelines or teacher imitation, limiting flexibility and decoupling learning from actual editing outcomes. The authors propose an experiential framework for long-horizon image editing in which a planner generates structured atomic decompositions and an orchestrator selects tools and regions to execute each step. A vision-language judge supplies outcome-based rewards for instruction adherence and visual quality; the orchestrator is trained to maximize these rewards, and successful trajectories are used to refine the planner, tightly coupling planning with reward-driven learning.
Paper Overview
Research area: Computer Vision (CV)
Authors: Anirudh Sundara Rajan, Krishna Kumar Singh, Yong Jae Lee
Published: 2026-05-14
arXiv: 2605.15181
Original Abstract
Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., "make this advertisement more vegetarian-friendly"). Prior agent based methods decompose such tasks but rely on handcrafted pipelines or teacher imitation, limiting flexibility and decoupling learning from actual editing outcomes. We propose an experiential framework for long-horizon image editing, where a planner generates structured atomic decompositions and an orchestrator selects tools and regions to execute each step. A vision language judge provides outcome-based rewards for instruction adherence and visual quality. The orchestrator is trained to maximize these rewards, and successful trajectories are used to refine the planner. By tightly coupling planning with reward d...(truncated)
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