  Recently, video conferencing apps have become functional by accomplishing
such computer vision-based features as real-time background removal and face
beautification. Limited variability in existing portrait segmentation and face
parsing datasets, including head poses, ethnicity, scenes, and occlusions
specific to video conferencing, motivated us to create a new dataset,
EasyPortrait, for these tasks simultaneously. It contains 40,000 primarily
indoor photos repeating video meeting scenarios with 13,705 unique users and
fine-grained segmentation masks separated into 9 classes. Inappropriate
annotation masks from other datasets caused a revision of annotator guidelines,
resulting in EasyPortrait's ability to process cases, such as teeth whitening
and skin smoothing. The pipeline for data mining and high-quality mask
annotation via crowdsourcing is also proposed in this paper. In the ablation
study experiments, we proved the importance of data quantity and diversity in
head poses in our dataset for the effective learning of the model. The
cross-dataset evaluation experiments confirmed the best domain generalization
ability among portrait segmentation datasets. Moreover, we demonstrate the
simplicity of training segmentation models on EasyPortrait without extra
training tricks. The proposed dataset and trained models are publicly
available.
