京公网安备11010802038645号京ICP备20022436号-3京EDI证京B2-20220639区块链京网信备 1101082276223860002X号以下为卖家选择提供的数据验证报告：
数据描述
CVPR 2023 Linking Garment With Person via Semantically Associated Landmarks for Virtual Try-On
The code is avaliable: https://www.modelscope.cn/models/damo/cv_SAL-VTON_virtual-try-on/summary
数据集描述 Dataset Description
名称 Name
基于语义关联的人体及服饰关键点数据集
Semantically Associated Landmarks for Human and Garment（SAL-HG）
简介 Brief Introduction
这个数据集是在VITON-HD、VITON、DressCode（上装部分）这三个用于虚拟试衣的开源数据集的图片上标注了语义关联的关键点。
这三个数据集中都包括服饰平铺图片和人像图片。
语义关联的含义是：人像和服饰中有着语义关联的一对的关键点。如下图中的A和A'
To obtain the semantically associated landmark dataset, we re-annotate images on the popular virtual try-on benchmark VITON-HD， VITON and DressCode（upper）datasets.
Each dataset consists of an in-shop garment image and an image of a person wearing the garment.
As can be observed from the below figure, the pixels around landmark A on the try-on result should come from the landmark A' area on the garment. Such a pair of landmarks with the same local semantics are called semantically associated landmarks.
标注规则 Label Rule
我们采用了一套统一的关键点标注规则，来为不同类型的服饰进行标注，也就是说，不同类型服饰图片中的相同的序号的关键点拥有一致的语义信息。
这是之前很多相关数据集所不具备的特点，过往的一些相关数据集中不同类型的服饰往往采用不同的关键点标注规则。
A unified labelling rule of landmarks is applied for diverse styles of garments.
To be specific, landmarks are defined according to different regions of garments and persons, so as to ensure that the landmarks of the same serial number on different types of garments have the same semantics.
如下图所示，每张图片都标注了32个关键点，以及3种不同的属性（可见性、遮挡性、存在性），3个属性彼此之间互斥。
As shown in the below figure, each image has 32 landmarks with several attributes (visible, occluded and absent).
When a region of the garment is lacking, the attributes of such landmarks in the lacking region become absent.
The sleeves of the right garment in the below figure are lacking, thus the attributes of landmarks in the upper arm region, elbow region, forearm region and sleeve opening region are absent.
样例 Samples
更多标注示例。
Additional samples are provied below.
和其他数据集比对 Compare with Other Datasets
下图是我们和其他相关数据集的比对，（a）DeepFasion and ULD（b）DeepFashion2（c）FashionAI（d）我们的SAL-HG。
Some samples are selected to show the differences between different clothing landmark datasets.(a) DeepFasion and ULD, (b) DeepFashion2, (c) FashionAI and (d) Ours.
数据集结构 Dataset Structure
关键点的标注存储在pkl格式的文件中, 数据内容示例如下
The annotations are stored in pkl files. An example of the data content is provided below.
import pickle
pkl_path = 'hd_train_person.pkl'
pkl_data = pickle.load(open(pkl_path, 'rb'))
for i in range(len(pkl_data)):
name = pkl_data[i]['name']
used = pkl_data[i]['used'] # if used == True, this annotation is valid
if used:
points = pkl_data[i]['points']
# [x, y, attribute]
# attrubute:1 visible, 0 occluded, -1 absent 
基于语义关联的人像和服饰关键点
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