# High-Accuracy Facial Depth Models derived from 3D Synthetic Data

arXiv: https://arxiv.org/abs/2003.06211 · HTML: https://mlsanity.com/paper/2003.06211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction](https://mlsanity.com/paper/2201.12170/) (2022)
- [A Dual-Feature Extractor Framework for Accurate Back Depth and Spine Morphology Estimation from Monocular RGB Images](https://mlsanity.com/paper/2507.22691/) (2025)
- [Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation](https://mlsanity.com/paper/2508.09188/) (2025)

## Abstract (arXiv)

In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding.