Cross-modal fashion synthesis and editing offer intelligent support to
fashion designers by enabling the automatic generation and local modification
of design drafts.While current diffusion models demonstrate commendable
stability and controllability in image synthesis,they still face significant
challenges in generating fashion design from abstract design elements and
fine-grained editing.Abstract sensory expressions, \eg office, business, and
party, form the high-level design concepts, while measurable aspects like
sleeve length, collar type, and pant length are considered the low-level
attributes of clothing.Controlling and editing fashion images using lengthy
text descriptions poses a difficulty.In this paper, we propose HieraFashDiff,a
novel fashion design method using the shared multi-stage diffusion model
encompassing high-level design concepts and low-level clothing attributes in a
hierarchical structure.Specifically, we categorized the input text into
different levels and fed them in different time step to the diffusion model
according to the criteria of professional clothing designers.HieraFashDiff
allows designers to add low-level attributes after high-level prompts for
interactive editing incrementally.In addition, we design a differentiable loss
function in the sampling process with a mask to keep non-edit
areas.Comprehensive experiments performed on our newly conducted Hierarchical
fashion dataset,demonstrate that our proposed method outperforms other
state-of-the-art competitors.

提出了一种名为 HieraFashDiff 的新型时尚设计方法，通过使用共享的多阶段扩散模型，包含高层设计概念和低层服装属性的层次结构，允许设计师在高级提示后逐步交互式编辑以添加低层属性。在我们新建的层次时尚数据集上进行的全面实验表明，我们提出的方法优于其他最先进的竞争方法。

多阶段扩散模型的层次化时装设计

Hierarchical Fashion Design with Multi-stage Diffusion Models

Existing fashion datasets do not consider the multi-facts that cause a
consumer to like or dislike a fashion image. Even two consumers like a same
fashion image, they could like this image for total different reasons. In this
paper, we study the reason why a consumer like a certain fashion image. Towards
this goal, we introduce an interpretability dataset, Fashionpedia-taste,
consist of rich annotation to explain why a subject like or dislike a fashion
image from the following 3 perspectives: 1) localized attributes; 2) human
attention; 3) caption. Furthermore, subjects are asked to provide their
personal attributes and preference on fashion, such as personality and
preferred fashion brands. Our dataset makes it possible for researchers to
build computational models to fully understand and interpret human fashion
taste from different humanistic perspectives and modalities.

本文研究了顾客喜欢某种时尚图像的原因。对此，作者引入了一种名为 Fashionpedia-taste 的可解释性数据集，该数据集包含了丰富的注释，解释了主体为什么喜欢或不喜欢某个时尚图像的原因。研究者可以利用我们的数据集来构建计算模型，从不同的人文角度和模态完全了解和解释人类时尚品味。

Fashionpedia-Taste：一个解释人类时尚品味的数据集

Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste

How do we determine whether two or more clothing items are compatible or
visually appealing? Part of the answer lies in understanding of visual
aesthetics, and is biased by personal preferences shaped by social attitudes,
time, and place. In this work we propose a method that predicts compatibility
between two items based on their visual features, as well as their context. We
define context as the products that are known to be compatible with each of
these item. Our model is in contrast to other metric learning approaches that
rely on pairwise comparisons between item features alone. We address the
compatibility prediction problem using a graph neural network that learns to
generate product embeddings conditioned on their context. We present results
for two prediction tasks (fill in the blank and outfit compatibility) tested on
two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon
dataset; we achieve state of the art results when using context information and
show how test performance improves as more context is used.

本文介绍一种基于图神经网络的方法，通过产品嵌入向量预测衣物单品或搭配的视觉配合性，相较于其他基于成对比较的模型，我们的模型涵盖更丰富的背景信息取得了业界领先的效果。