超越瑕疵：一种用于 VTON 和姿态迁移端到端伪影去除的条件修复方法
Beyond Imperfections: A Conditional Inpainting Approach for End-to-End Artifact Removal in VTON and Pose Transfer
AI总结：
本文提出首个端到端条件修复框架，用于检测并去除虚拟试穿和姿态迁移中的伪影，提升图像视觉质量，并构建了带掩码的专用数据集，实验验证其有效性与优越性。
摘要：
伪影常常降低虚拟试穿（VTON）和姿态迁移应用的视觉质量，影响用户体验。本研究引入了一种新颖的条件修复技术，旨在检测并去除此类失真，提升图像美感。我们的工作是首个针对这一具体问题提出端到端框架的研究，并开发了一个包含VTON和姿态迁移任务中伪影的专门数据集，附有突出受影响区域的掩码。实验结果表明，我们的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，为计算机视觉和图像处理领域树立了新基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
