# EasyPortrait -- Face Parsing and Portrait Segmentation Dataset

arXiv: https://arxiv.org/abs/2304.13509 · HTML: https://mlsanity.com/paper/2304.13509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Privacy-Preserving Portrait Matting](https://mlsanity.com/paper/2104.14222/) (2021)
- [PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency](https://mlsanity.com/paper/2105.09180/) (2021)
- [RoI Tanh-polar Transformer Network for Face Parsing in the Wild](https://mlsanity.com/paper/2102.02717/) (2021)
- [PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset](https://mlsanity.com/paper/2112.07146/) (2021)
- [A Deep Face Identification Network Enhanced by Facial Attributes Prediction](https://mlsanity.com/paper/1805.00324/) (2018)
- [SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder](https://mlsanity.com/paper/1911.09099/) (2019)
- [ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules](https://mlsanity.com/paper/1908.03093/) (2019)
- [Flow-based Video Segmentation for Human Head and Shoulders](https://mlsanity.com/paper/2104.09752/) (2021)
- [FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction](https://mlsanity.com/paper/2201.08425/) (2022)

## Abstract (arXiv)

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.