TD-GEM：文本驱动的服装编辑映射器
TD-GEM: Text-Driven Garment Editing Mapper
AI总结：
提出TD-GEM，一种基于文本提示的服装编辑优化方法，利用GAN反演和CLIP引导在解耦潜在空间中准确编辑时尚图像，在颜色和袖长属性上生成逼真效果。
摘要：
基于语言的时尚图像编辑允许用户通过提供的文本提示尝试所需服装的变体。受StyleCLIP和HairCLIP中操纵潜在表示研究的启发，我们专注于这些潜在空间，以编辑全身人体数据集的时尚物品。目前，由于服装形状和纹理的复杂性以及人体姿态的多样性，处理时尚图像编辑存在空白。在本文中，我们提出了一种名为文本驱动服装编辑映射器（TD-GEM）的编辑优化器方案方法，旨在以解耦的方式编辑时尚物品。为此，我们首先通过生成对抗网络反演（如用于编辑的编码器（e4e）或关键调整反演（PTI））获得图像的潜在表示，以获得更准确的结果。然后利用基于优化的对比语言-图像预训练（CLIP）来引导时尚图像的潜在表示朝向以文本提示表达的目标属性的方向。我们的TD-GEM根据目标属性准确操纵图像，同时保持图像的其他部分不变。在实验中，我们在两个不同属性（即“颜色”和“袖长”）上评估TD-GEM，与最近的操纵方案相比，它有效地生成了逼真的图像。
英文摘要：
Language-based fashion image editing allows users to try out variations of desired garments through provided text prompts. Inspired by research on manipulating latent representations in StyleCLIP and HairCLIP, we focus on these latent spaces for editing fashion items of full-body human datasets. Currently, there is a gap in handling fashion image editing due to the complexity of garment shapes and textures and the diversity of human poses. In this paper, we propose an editing optimizer scheme method called Text-Driven Garment Editing Mapper (TD-GEM), aiming to edit fashion items in a disentangled way. To this end, we initially obtain a latent representation of an image through generative adversarial network inversions such as Encoder for Editing (e4e) or Pivotal Tuning Inversion (PTI) for more accurate results. An optimization-based Contrastive Language-Image Pre-training (CLIP) is then utilized to guide the latent representation of a fashion image in the direction of a target attribute expressed in terms of a text prompt. Our TD-GEM manipulates the image accurately according to the target attribute, while other parts of the image are kept untouched. In the experiments, we evaluate TD-GEM on two different attributes (i.e., "color" and "sleeve length"), which effectively generates realistic images compared to the recent manipulation schemes.
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。
