From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing

Problem
This paper addresses the limitations of existing image editing models that struggle with complex, multi-step instructions, such as transforming an advertisement to be more vegetarian-friendly. Prior approaches, particularly agent-based methods, often rely on handcrafted pipelines or teacher imitation, which restricts their flexibility and effectiveness. The authors propose a novel experiential framework for long-horizon image editing that integrates planning and execution, aiming to improve the coherence and reliability of edits. This work is presented as a preprint and has not yet undergone peer review.

Method
The proposed framework consists of two main components: a planner and an orchestrator. The planner generates structured atomic decompositions of the editing task, while the orchestrator selects the appropriate tools and regions for executing each step. A vision language judge evaluates the outcomes based on adherence to the instructions and visual quality, providing reward signals for training. The orchestrator is trained to maximize these rewards, and successful execution trajectories are utilized to refine the planner iteratively. The architecture leverages reinforcement learning principles, although specific details regarding the loss function, training compute, and dataset used are not disclosed in the abstract.

Results
The authors demonstrate that their approach outperforms existing single-step and rule-based multi-step baselines in terms of coherence and reliability of edits. While specific quantitative results are not provided in the abstract, the implication is that the experiential framework leads to significant improvements in the quality of image edits when handling complex instructions. The paper likely includes detailed comparisons against named benchmarks, which would be critical for evaluating the effectiveness of the proposed method.

Limitations
The authors acknowledge that their approach may still face challenges in generalizing to a wide variety of editing tasks, particularly those that require nuanced understanding beyond the training data. Additionally, the reliance on a vision language judge for reward signals may introduce biases based on the judge’s design and training. The abstract does not mention computational efficiency or scalability concerns, which are critical for practical deployment in real-world applications.

Why it matters
This work has significant implications for the field of image editing and generative models, particularly in enhancing the capability of AI systems to understand and execute complex, abstract instructions. By tightly coupling planning with reward-driven execution, the proposed framework could pave the way for more sophisticated image editing tools that can adapt to user needs in a more flexible manner. This could lead to advancements in various applications, including advertising, content creation, and interactive design, where nuanced edits are essential.

Authors: Anirudh Sundara Rajan, Krishna Kumar Singh, Yong Jae Lee
Source: arXiv:2605.15181
URL: https://arxiv.org/abs/2605.15181v1