Task-Decoupled Image Inpainting Framework for Class-specific Object Remover
Changsuk Oh, H. Jin Kim

TL;DR
This paper introduces a task-decoupled framework for class-specific object removal in images, training separate models for object restoration and removal to improve removal quality over traditional inpainting methods.
Contribution
It proposes a novel task-decoupled inpainting framework with separate models for restoration and removal, and a data curation method for training class-specific object removers.
Findings
The class-specific object remover outperforms general inpainting-based removers.
The framework effectively isolates object removal and restoration tasks.
Experimental results demonstrate improved removal accuracy on multiple datasets.
Abstract
Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance. Existing works on object removal erase removal targets using image inpainting networks. However, image inpainting networks often generate unsatisfactory removal results. In this work, we find that the current training approach which encourages a single image inpainting model to handle both object removal and restoration tasks is one of the reasons behind such unsatisfactory result. Based on this finding, we propose a task-decoupled image inpainting framework which generates two separate inpainting models: an object restorer for object restoration tasks and an object remover for object removal tasks. We train the object restorer with the masks that partially cover the removal targets. Then, the proposed framework makes an object restorer to generate a guidance for…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
MethodsInpainting
