Abstract
Object removal requires eliminating not only the target object but also its effects, such as shadows and reflections. However, diffusion-based inpainting methods often produce artifacts, hallucinate content, alter background, and struggle to remove object effects accurately. To address this challenge, we introduce a new dataset for OBject-Effect Removal, named OBER, which provides paired images with and without object effects, along with precise masks for both objects and their associated visual artifacts. The dataset comprises high-quality captured and simulated data, covering diverse object categories and complex multi-object scenes. Building on OBER, we propose a novel framework, ObjectClear, which incorporates an object-effect attention mechanism to guide the model toward the foreground removal regions by learning attention masks, effectively decoupling foreground removal from background reconstruction. Furthermore, the predicted attention map enables an attention-guided fusion strategy during inference, greatly preserving background details. Extensive experiments demonstrate that ObjectClear outperforms existing methods, achieving improved object-effect removal quality and background fidelity, especially in complex scenarios.
Abstract (translated)
对象移除不仅需要消除目标物体，还需要处理其影响，如阴影和反射。然而，基于扩散的修复方法常常会产生伪影、生成虚构内容、改变背景，并且难以准确去除物体效果。为了解决这一挑战，我们引入了一个新的数据集OBject-Effect Removal（OBER），该数据集提供了包含与不包含物体效应的成对图像，以及精确的对象及其相关视觉瑕疵掩码。此数据集包括高质量的真实拍摄和模拟数据，涵盖了多种对象类别及复杂的多对象场景。 基于OBER数据集，我们提出了一个创新框架ObjectClear，它结合了对象影响注意机制，通过学习关注掩模来指导模型聚焦于前景移除区域，从而有效地将前景移除与背景重建解耦。此外，在推理阶段利用预测的注意力图能够实现一种引导融合策略，大大保留了背景细节。 广泛实验表明，ObjectClear在复杂场景中显著优于现有方法，实现了改进的对象影响去除质量和背景保真度。
URL
https://arxiv.org/abs/2505.22636
