Recently, due to COVID-19 and the growing demand for remote work, video conferencing apps have become especially widespread. The most valuable features of video chats are real-time background removal and face beautification. While solving these tasks, computer vision researchers face the problem of having relevant data for the training stage. There is no large dataset with high-quality labeled and diverse images of people in front of a laptop or smartphone camera to train a lightweight model without additional approaches. To boost the progress in this area, we provide a new image dataset, EasyPortrait, for portrait segmentation and face parsing tasks. It contains 20,000 primarily indoor photos of 8,377 unique users, and fine-grained segmentation masks separated into 9 classes. Images are collected and labeled from crowdsourcing platforms. Unlike most face parsing datasets, in EasyPortrait, the beard is not considered part of the skin mask, and the inside area of the mouth is separated from the teeth. These features allow using EasyPortrait for skin enhancement and teeth whitening tasks. This paper describes the pipeline for creating a large-scale and clean image segmentation dataset using crowdsourcing platforms without additional synthetic data. Moreover, we trained several models on EasyPortrait and showed experimental results. Proposed dataset and trained models are publicly available.

本文介绍了使用众包平台创建大规模、干净的图像分割数据集的流程，并在EasyPortrait数据集上训练多个模型并展示实验结果，EasyPortrait包含了20000张主要的室内照片，分成9类，并可用于皮肤增强和牙齿美白等任务。

EasyPortrait - 人脸解析与肖像分割数据集