# Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

arXiv: https://arxiv.org/abs/2205.06218 · HTML: https://mlsanity.com/paper/2205.06218/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution](https://mlsanity.com/paper/2012.00739/) (2020)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [Analysis of Hand Segmentation in the Wild](https://mlsanity.com/paper/1803.03317/) (2018)
- [On Recognizing Occluded Faces in the Wild](https://mlsanity.com/paper/2109.03672/) (2021)
- [Real-Time Facial Segmentation and Performance Capture from RGB Input](https://mlsanity.com/paper/1604.02647/) (2016)
- [AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces](https://mlsanity.com/paper/1706.04277/) (2017)
- [Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions](https://mlsanity.com/paper/1708.00370/) (2017)
- [Segmentation-Reconstruction-Guided Facial Image De-occlusion](https://mlsanity.com/paper/2112.08022/) (2021)
- [FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction](https://mlsanity.com/paper/2201.08425/) (2022)
- [Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation](https://mlsanity.com/paper/2006.13980/) (2020)
- [Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2112.14382/) (2021)

## Built on by (most cited first, 11 of 11)

- [Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring](https://mlsanity.com/paper/2303.08536/) (2023)
- [3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow](https://mlsanity.com/paper/2404.09819/) (2024)
- [DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment](https://mlsanity.com/paper/2305.11522/) (2023)
- [Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation](https://mlsanity.com/paper/2504.18539/) (2025)
- [AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction](https://mlsanity.com/paper/2304.13115/) (2023)
- [Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses](https://mlsanity.com/paper/2510.13281/) (2025)
- [Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation](https://mlsanity.com/paper/2209.14479/) (2022)
- [OFER: Occluded Face Expression Reconstruction](https://mlsanity.com/paper/2410.21629/) (2024)
- [Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation](https://mlsanity.com/paper/2508.03055/) (2025)
- [APPLE: Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping](https://mlsanity.com/paper/2601.15288/) (2026)
- [S3POT: Contrast-Driven Face Occlusion Segmentation via Self-Supervised Prompt Learning](https://mlsanity.com/paper/2602.00635/) (2026)

## Abstract (arXiv)

This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-intensive. Although some efforts have been made in synthetic data generation, the naturalistic aspect of data remains less explored. In our study, we propose two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces; and Random Occlusion Generation (RandOcc), a more general synthetic occluded data generation method. We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. To facilitate model evaluation, we present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild, featuring both careful alignment preprocessing and an in-the-wild setting for robustness test. We further conduct a comprehensive analysis on a newly introduced segmentation benchmark, offering insights for future exploration.