提出端到端修复方法，专治虚拟试衣与姿态迁移中的图像瑕疵。
Beyond Imperfections: A Conditional Inpainting Approach for End-to-End Artifact Removal in VTON and Pose Transfer
- 基于条件修复技术，自动定位并消除图像瑕疵区域。
- 在自建数据集上实现显著视觉质量提升，优于现有方法。
- 适合图像生成、虚拟试衣等需要高画质的应用场景。
虚似试衣（VTON）和姿态迁移应用中，伪影常降低图像视觉质量，影响用户体验。本文提出一种新型条件修复方法，旨在检测并移除这些失真，提升图像美观度。这是首个针对该问题的端到端框架，同时构建了包含掩码标注的专用伪影数据集。实验表明，该方法不仅能有效去除伪影，还能显著提升最终图像的视觉质量，为计算机视觉与图像处理树立新基准。
原文摘要 · Abstract (English)
Artifacts often degrade the visual quality of virtual try-on (VTON) and pose transfer applications, impacting user experience. This study introduces a novel conditional inpainting technique designed to detect and remove such distortions, improving image aesthetics. Our work is the first to present an end-to-end framework addressing this specific issue, and we developed a specialized dataset of artifacts in VTON and pose transfer tasks, complete with masks highlighting the affected areas. Experimental results show that our method not only effectively removes artifacts but also significantly enhances the visual quality of the final images, setting a new benchmark in computer vision and image processing.
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