# Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction

arXiv: https://arxiv.org/abs/2412.19848 · HTML: https://mlsanity.com/paper/2412.19848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 25 Dec 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [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)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Morphable Face Models - An Open Framework](https://mlsanity.com/paper/1709.08398/) (2017)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network](https://mlsanity.com/paper/1612.04904/) (2016)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [3D Face Reconstruction by Learning from Synthetic Data](https://mlsanity.com/paper/1609.04387/) (2016)
- [Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation](https://mlsanity.com/paper/1703.10131/) (2017)
- [Video Generation from Single Semantic Label Map](https://mlsanity.com/paper/1903.04480/) (2019)
- [Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks](https://mlsanity.com/paper/2003.05653/) (2020)
- [Photo-Realistic Facial Details Synthesis from Single Image](https://mlsanity.com/paper/1903.10873/) (2019)
- [Learning from Millions of 3D Scans for Large-scale 3D Face Recognition](https://mlsanity.com/paper/1711.05942/) (2017)
- [Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment](https://mlsanity.com/paper/1802.06713/) (2018)
- [3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach](https://mlsanity.com/paper/1411.2942/) (2014)

## Built on by (most cited first, 1 of 1)

- [Unwarping the Lens: A Physics-Grounded Approach to Video Glasses Removal](https://mlsanity.com/paper/2608.20212/) (2026)

## Abstract (arXiv)

Single-view 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the input is unobstructed faces which makes their method not suitable for in-the-wild conditions. We present a method for performing a 3D face that removes eyeglasses from a single image. Existing facial reconstruction methods fail to remove eyeglasses automatically for generating a photo-realistic 3D face "in-the-wild".The innovation of our method lies in a process for identifying the eyeglasses area robustly and remove it intelligently. In this work, we estimate the 2D face structure of the reasonable position of the eyeglasses area, which is used for the construction of 3D texture. An excellent anti-eyeglasses face reconstruction method should ensure the authenticity of the output, including the topological structure between the eyes, nose, and mouth. We achieve this via a deep learning architecture that performs direct regression of a 3DMM representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related face parsing task can be incorporated into the proposed framework and help improve reconstruction quality. We conduct extensive experiments on existing 3D face reconstruction tasks as concrete examples to demonstrate the method's superior regulation ability over existing methods often break down.