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 Contrasive 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.

本文提出了一种名为 TD-GEM 的文本驱动服装编辑方案方法，通过生成对抗网络反演获取数据图像的潜在表达，之后通过对比学习的语言图像预训练（CLIP）指导一个服装图像的潜在表达，以在保持图像其他部分不变的情况下编辑与文本要求一致的服装项目，该方案通过对颜色和袖长进行实验，生成了更逼真的图像。

TD-GEM: 文本驱动的服装编辑映射器

TD-GEM: Text-Driven Garment Editing Mapper

Fashion-image editing represents a challenging computer vision task, where
the goal is to incorporate selected apparel into a given input image. Most
existing techniques, known as Virtual Try-On methods, deal with this task by
first selecting an example image of the desired apparel and then transferring
the clothing onto the target person. Conversely, in this paper, we consider
editing fashion images with text descriptions. Such an approach has several
advantages over example-based virtual try-on techniques, e.g.: (i) it does not
require an image of the target fashion item, and (ii) it allows the expression
of a wide variety of visual concepts through the use of natural language.
Existing image-editing methods that work with language inputs are heavily
constrained by their requirement for training sets with rich attribute
annotations or they are only able to handle simple text descriptions. We
address these constraints by proposing a novel text-conditioned editing model,
called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of
diverse text descriptions to guide the editing procedure. Specifically with
FICE, we augment the common GAN inversion process by including semantic,
pose-related, and image-level constraints when generating images. We leverage
the capabilities of the CLIP model to enforce the semantics, due to its
impressive image-text association capabilities. We furthermore propose a
latent-code regularization technique that provides the means to better control
the fidelity of the synthesized images. We validate FICE through rigorous
experiments on a combination of VITON images and Fashion-Gen text descriptions
and in comparison with several state-of-the-art text-conditioned image editing
approaches. Experimental results demonstrate FICE generates highly realistic
fashion images and leads to stronger editing performance than existing
competing approaches.

本论文提出了一种新的文本引导时尚图片编辑方法 FICE，包括 GAN 反演、CLIP 模型语义信息和姿态、图片评估等多项约束，使其能够更好地控制图像合成的逼真程度，并与多种先进方法进行对比验证，结果表明 FICE 具有更强的编辑表现和高度逼真的时尚图像生成能力。