Title: Interaction-Consistent Object Removal via MLLM-Based Reasoning
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Language-guided Image editing, MLLM

Abstract: Image-based object removal often erases only the named target, leaving behind interaction evidence that renders the result semantically inconsistent. We formalize this problem as Interaction-Consistent Object Removal (ICOR), which requires removing not only the target object but also associated interaction elements, such as lighting-dependent effects, physically connected objects, target-produced elements, and contextually linked objects. To address this task, we propose Reasoning-Enhanced Object Removal with MLLM (REORM), a reasoning-enhanced object removal framework that leverages multimodal large language models to infer which elements must be jointly removed. REORM features a modular design that integrates MLLM-driven analysis, mask-guided removal, and a self-correction mechanism, along with a local-deployment variant that supports accurate editing under limited resources. To support evaluation, we introduce ICOREval, a benchmark consisting of instruction-driven removals with rich interaction dependencies. On ICOREval, REORM outperforms representative image editing systems, demonstrating its effectiveness in producing interaction-consistent results.

TL;DR: This paper presents REORM, a reasoning-enhanced object removal framework that uses MLLMs to remove both target objects and their associated interaction effects, achieving interaction-consistent results on the ICOREval benchmark.