# Normal and Visibility Estimation of Human Face from a Single Image

arXiv: https://arxiv.org/abs/2203.04647 · HTML: https://mlsanity.com/paper/2203.04647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild](https://mlsanity.com/paper/1712.01261/) (2017)
- [Learning Detailed Face Reconstruction from a Single Image](https://mlsanity.com/paper/1611.05053/) (2016)
- [Neural Face Editing with Intrinsic Image Disentangling](https://mlsanity.com/paper/1704.04131/) (2017)
- [On Learning 3D Face Morphable Model from In-the-wild Images](https://mlsanity.com/paper/1808.09560/) (2018)
- [Photorealistic Facial Texture Inference Using Deep Neural Networks](https://mlsanity.com/paper/1612.00523/) (2016)
- [Real-time Monte Carlo Denoising with Weight Sharing Kernel Prediction Network](https://mlsanity.com/paper/2202.05977/) (2022)
- [Morphological Anti-Aliasing Method for Boundary Slope Prediction](https://mlsanity.com/paper/2203.03870/) (2022)

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

- [Neural Rendering and Its Hardware Acceleration: A Review](https://mlsanity.com/paper/2402.00028/) (2024)

## Abstract (arXiv)

Recent work on the intrinsic image of humans starts to consider the visibility of incident illumination and encodes the light transfer function by spherical harmonics. In this paper, we show that such a light transfer function can be further decomposed into visibility and cosine terms related to surface normal. Such decomposition allows us to recover the surface normal in addition to visibility. We propose a deep learning-based approach with a reconstruction loss for training on real-world images. Results show that compared with previous works, the reconstruction of human face from our method better reveals the surface normal and shading details especially around regions where visibility effect is strong.