This paper proposes a novel garment transfer method supervised with knowledge distillation from virtual try-on. Our method first reasons the transfer parsing to provide shape prior to downstream tasks. We employ a multi-phase teaching strategy to supervise the training of the transfer parsing reasoning model, learning the response and feature knowledge from the try-on parsing reasoning model. To correct the teaching error, it transfers the garment back to its owner to absorb the hard knowledge in the self-study phase. Guided by the transfer parsing, we adjust the position of the transferred garment via STN to prevent distortion. Afterward, we estimate a progressive flow to precisely warp the garment with shape and content correspondences. To ensure warping rationality, we supervise the training of the garment warping model using target shape and warping knowledge from virtual try-on. To better preserve body features in the transfer result, we propose a well-designed training strategy for the arm regrowth task to infer new exposure skin. Experiments demonstrate that our method has state-of-the-art performance compared with other virtual try-on and garment transfer methods in garment transfer, especially for preserving garment texture and body features.

翻译：本文提出一种通过虚拟试穿知识蒸馏监督的新型服装迁移方法。该方法首先推理迁移解析为下游任务提供形状先验，采用多阶段教学策略监督迁移解析推理模型的训练，从试穿解析推理模型中学习响应与特征知识。为修正教学误差，通过将服装回迁至原穿着者完成自修阶段硬知识的吸收。在迁移解析引导下，利用STN调整迁移服装位置以防形变，随后估计渐进式流场建立服装形状与内容的精确对应翘曲。为保证翘曲合理性，利用虚拟试穿的目标形状与翘曲知识监督服装翘曲模型训练。为更好保留迁移结果中的身体特征，针对手臂再生任务设计训练策略以推断新暴露皮肤。实验表明，本方法在服装迁移性能上优于现有虚拟试穿与服装迁移方法，尤其在服装纹理与身体特征保持方面达到最先进水平。