# Learning Contour-Guided 3D Face Reconstruction with Occlusions

arXiv: https://arxiv.org/abs/2503.12494 · HTML: https://mlsanity.com/paper/2503.12494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network](https://mlsanity.com/paper/1612.04904/) (2016)
- [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis](https://mlsanity.com/paper/1611.09969/) (2016)
- [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)
- [Eye In-Painting with Exemplar Generative Adversarial Networks](https://mlsanity.com/paper/1712.03999/) (2017)
- [Face Parsing via a Fully-Convolutional Continuous CRF Neural Network](https://mlsanity.com/paper/1708.03736/) (2017)
- [End-to-End Face Parsing via Interlinked Convolutional Neural Networks](https://mlsanity.com/paper/2002.04831/) (2020)

## Abstract (arXiv)

Recently, deep learning-based 3D face reconstruction methods have demonstrated promising advancements in terms of quality and efficiency. Nevertheless, these techniques face challenges in effectively handling occluded scenes and fail to capture intricate geometric facial details. Inspired by the principles of GANs and bump mapping, we have successfully addressed these issues. Our approach aims to deliver comprehensive 3D facial reconstructions, even in the presence of occlusions.While maintaining the overall shape's robustness, we introduce a mid-level shape refinement to the fundamental structure. Furthermore, we illustrate how our method adeptly extends to generate plausible details for obscured facial regions. We offer numerous examples that showcase the effectiveness of our framework in producing realistic results, where traditional methods often struggle. To substantiate the superior adaptability of our approach, we have conducted extensive experiments in the context of general 3D face reconstruction tasks, serving as concrete evidence of its regulatory prowess compared to manual occlusion removal methods.