# Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data arXiv: https://arxiv.org/abs/2203.10474 ยท HTML: https://mlsanity.com/paper/2203.10474/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 45 of its 63 references that are in the map. - First posted on arXiv 20 Mar 2022. ## Builds on (most cited first, 30 of 45) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016) - [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017) - [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019) - [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017) - [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016) - [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018) - [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017) - [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018) - [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020) - [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018) - [StyleRig: Rigging StyleGAN for 3D Control over Portrait Images](https://mlsanity.com/paper/2004.00121/) (2020) - [FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation](https://mlsanity.com/paper/1612.02649/) (2016) - [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017) - [Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning](https://mlsanity.com/paper/2004.11660/) (2020) - [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019) - [Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach](https://mlsanity.com/paper/1812.05040/) (2018) - [No More Discrimination: Cross City Adaptation of Road Scene Segmenters](https://mlsanity.com/paper/1704.08509/) (2017) - [Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1707.09465/) (2017) - [Deep Feature Interpolation for Image Content Changes](https://mlsanity.com/paper/1611.05507/) (2016) - [Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data](https://mlsanity.com/paper/1802.10151/) (2018) - [Towards Open-Set Identity Preserving Face Synthesis](https://mlsanity.com/paper/1803.11182/) (2018) - [Image-to-image Translation via Hierarchical Style Disentanglement](https://mlsanity.com/paper/2103.01456/) (2021) ## Built on by (most cited first, 11 of 11) - [SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection](https://mlsanity.com/paper/2308.08935/) (2023) - [DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning](https://mlsanity.com/paper/2504.14509/) (2025) - [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024) - [M3Act: Learning from Synthetic Human Group Activities](https://mlsanity.com/paper/2306.16772/) (2023) - [End-to-end Face-swapping via Adaptive Latent Representation Learning](https://mlsanity.com/paper/2303.04186/) (2023) - [V-LASIK: Consistent Glasses-Removal from Videos Using Synthetic Data](https://mlsanity.com/paper/2406.14510/) (2024) - [Structure-Guided Diffusion Models for High-Fidelity Portrait Shadow Removal](https://mlsanity.com/paper/2507.04692/) (2025) - [Beyond Shadows: A Large-Scale Benchmark and Multi-Stage Framework for High-Fidelity Facial Shadow Removal](https://mlsanity.com/paper/2601.19309/) (2026) - [High-Quality Face Caricature via Style Translation](https://mlsanity.com/paper/2311.13338/) (2023) - [Calibration of 3D Single-pixel Imaging Systems with a Calibration Field](https://mlsanity.com/paper/2410.07545/) (2024) - [Unwarping the Lens: A Physics-Grounded Approach to Video Glasses Removal](https://mlsanity.com/paper/2608.20212/) (2026) ## Abstract (arXiv) In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical in handling these problems. However, completely removing the eyeglasses is challenging because the lighting effects (e.g., cast shadows) caused by them are often complex. In this paper, we propose a novel framework to remove eyeglasses as well as their cast shadows from face images. The method works in a detect-then-remove manner, in which eyeglasses and cast shadows are both detected and then removed from images. Due to the lack of paired data for supervised training, we present a new synthetic portrait dataset with both intermediate and final supervisions for both the detection and removal tasks. Furthermore, we apply a cross-domain technique to fill the gap between the synthetic and real data. To the best of our knowledge, the proposed technique is the first to remove eyeglasses and their cast shadows simultaneously. The code and synthetic dataset are available at https://github.com/StoryMY/take-off-eyeglasses.