PAPER_TITLE

FIRST_AUTHOR_LAST, FIRST_AUTHOR_FIRST; SECOND_AUTHOR_LAST, SECOND_AUTHOR_FIRST

WorldEdit: Towards Open-World Image Editing With a Knowledge-Informed Benchmark

Anonymous Authors

Abstract

Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synthesis. However, these models often face challenges when dealing with implicit editing instructions, which describe the cause of a visual change without explicitly detailing the resulting outcome. These limitations arise because existing models rely on uniform editing strategies that are not equipped to handle the complex world knowledge and reasoning required for implicit instructions. To address this gap, we introduce WorldEdit, a dataset specifically designed to enable world-driven image editing. WorldEdit consists of high-quality editing samples, guided by paraphrased instructions that align with real-world causal logic. Furthermore, we provide WorldEdit-Test for evaluating the existing model’s performance on causal editing scenarios. With WorldEdit, we use a two-stage training framework for fine-tuning models like Bagel, integrating with a causal verification reward. Our results show that the proposed dataset and methods significantly narrow the gap with GPT-4o and Nano-Banana, demonstrating competitive performance not only in instruction following but also in knowledge plausibility, where many open-source systems typically struggle.

Unlike traditional image editing (left), which adopts a uniform editing strategy for different editing objects, world editing (right) needs to take into account the nature of the editing objects in the real world and produce editing results that conform to causal logic.

Qualitative results on WorldEdit-Test with paraphrased instructions. Text alone often fails to capture fine-grained causal details (e.g., scattering pattern of collapsed building blocks), and models vary in their ability to interpret such prompts. Our model, fine-tuned with WorldEdit, generates the most faithful and visually coherent images, underscoring the importance of high-quality world knowledge-driven data.

Qualitative comparison across different causal categories. The figure shows representative examples from ten causal reasoning tasks. Each row corresponds to a causal scenario, with the source image on the left followed by results from different models. Our method generates outputs that are both visually plausible and causally coherent, whereas baselines often produce irrelevant or stylistic edits, failing to reflect the causal logic of the instruction.