Object removal requires eliminating not only the target object but also its associated visual effects such as shadows and reflections. However, diffusion-based inpainting and removal methods often introduce artifacts, hallucinate contents, alter background, and struggle to remove object effects accurately. To address these challenges, we propose ObjectClear, a novel framework that decouples foreground removal from background reconstruction via an adaptive target-aware attention mechanism. This design empowers the model to precisely localize and remove both objects and their effects while maintaining high background fidelity. Moreover, the learned attention maps are leveraged for an attention-guided fusion strategy during inference, further enhancing visual consistency. To facilitate the training and evaluation, we construct OBER, a large-scale dataset for OBject-Effect Removal, which provides paired images with and without object-effects, along with precise masks for both objects and their effects. The dataset comprises high-quality captured and simulated data, covering diverse objects, effects, and complex multi-object scenes. Extensive experiments demonstrate that ObjectClear outperforms prior methods, achieving superior object-effect removal quality and background fidelity, especially in challenging scenarios.

翻译：目标移除不仅需要消除目标物体本身，还需消除其相关的视觉效应，如阴影和反射。然而，基于扩散的修复与移除方法常会引入伪影、产生幻觉内容、改变背景，且难以准确移除目标效应。为解决这些挑战，我们提出了ObjectClear——一种通过自适应目标感知注意力机制将前景移除与背景重建解耦的新型框架。该设计使模型能够精确定位并移除目标及其效应，同时保持高度的背景保真度。此外，在推理阶段，我们利用学习到的注意力图进行注意力引导的融合策略，进一步提升了视觉一致性。为促进训练与评估，我们构建了OBER——一个用于目标-效应移除的大规模数据集，该数据集提供了包含与不包含目标效应的配对图像，以及目标物体及其效应的精确掩码。该数据集包含高质量采集与模拟数据，涵盖多样化的物体、效应及复杂的多目标场景。大量实验表明，ObjectClear在目标效应移除质量与背景保真度方面均优于现有方法，尤其在具有挑战性的场景中表现更为突出。