SIMON: SImultaneous Multi-Object Navigation

This paper tackles the challenge of simultaneously manipu-lating multiple objects in image editing—a scenario that extends beyondthe capabilities of existing methods focused on single-object manipula-tion. Our approach SIMON efficiently handles complex rearrangementssuch as object shuffling, achieving higher visual quality at both image andregion levels, while reducing inference time. To this end, we propose threekey modules: a content-aware attention mechanism for region-adaptivefocus, a multi-object energy guidance strategy for subtask-specific con-sistency, and a latent initialization technique for artifact suppression.Together, these components enhance visual coherence across inpainting,object relocation, and background preservation subtasks. To evaluate ourframework, we curate 1,000 images from the 3D-FUTURE dataset, eachcontaining 2 to 5 target objects. For every object, we annotate a newtarget location, ensuring that the resulting layouts are physically plausi-ble and semantically meaningful. We conduct comparative experimentsagainst three state-of-the-art methods, using both image-level, region-level, and instance-level metrics. Both quantitative results and humananalysis confirm that our framework delivers significant improvementsin both visual quality and computational efficiency. We further validateour design through extensive ablation studies with diverse architecturesand applications. The code and annotated dataset are publicly avail-able at https://github.com/yijichar/SIMON-SImultaneous-Multi-Object-Navigation.