# Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

## TL;DR

This paper introduces an image-to-image translation neural network that reconstructs detailed 3D facial geometry from a single image, overcoming limitations of low-dimensional models by directly predicting depth and correspondence maps.

## Contribution

It presents a novel pixel-based approach for face reconstruction that handles diverse expressions and is trained solely on synthetic data, improving expressiveness and robustness.

## Key findings

- High-quality reconstructions of diverse faces achieved
- Effective on in-the-wild images with extreme expressions
- Outperforms traditional low-dimensional subspace methods

## Abstract

It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative, we propose an Image-to-Image translation network that jointly maps the input image to a depth image and a facial correspondence map. This explicit pixel-based mapping can then be utilized to provide high quality reconstructions of diverse faces under extreme expressions, using a purely geometric refinement process. In the spirit of recent approaches, the network is trained only with synthetic data, and is then evaluated on in-the-wild facial images. Both qualitative and quantitative analyses demonstrate the accuracy and the robustness of our approach.