DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenes

2026-07-18

AI Summary

This work addresses the challenge of removing specified objects in dense scenes, where existing methods often suffer from semantic interference caused by visually similar instances, leading to incomplete removal or duplicated artifacts. To overcome this limitation, the authors propose DORS, a diffusion-based object removal framework that introduces a novel dynamic attention routing mechanism comprising Instance Filtering Attention (IFA) and Context-Guided Routing (CGR). This mechanism enables fine-grained control over the attention space, effectively suppressing interference while preserving scene consistency. Furthermore, the study presents DOR-Bench, the first benchmark specifically designed for evaluating dense object removal. Experimental results demonstrate that DORS significantly outperforms current state-of-the-art methods, achieving superior removal completeness and visual coherence.

Abstract

Object removal aims to eliminate target objects specified by a mask while preserving visual consistency with the surrounding regions. Existing methods typically rely on contextual information from surrounding regions. However, in dense scenes where the surrounding regions contain instances visually similar to the removal target, such reliance often leads to semantic interference, resulting in incomplete removal. This problem arises from erroneous information propagation in the attention space, where masked queries tend to align with such instances due to global similarity matching in self-attention. To address this challenge, we propose a Diffusion-based Object Removal framework for dense Scenes, dubbed DORS, built upon a Dynamic Attention Routing mechanism comprising two complementary components: Instance-Filtered Attention (IFA), which suppresses misleading semantic information from similar instances through dynamically constructed mask-guided attention constraints, and Context-Guided Routing (CGR), which dynamically routes complementary scene information to maintain visual consistency. We further introduce DOR-Bench, a benchmark tailored for object removal in dense scenes. Extensive experiments demonstrate that DORS outperforms state-of-the-art methods, particularly in reducing incomplete removal and duplicate artifacts. The code will be available at https://github.com/httang1224/DORS.

Problem

• object removal
• dense scenes
• semantic interference
• incomplete removal
• attention mechanism

Innovation

• Dynamic Attention Routing
• Diffusion-based Object Removal
• Instance-Filtered Attention
• Context-Guided Routing
• Dense Scenes