# Improving Face Detection Performance with 3D-Rendered Synthetic Data

arXiv: https://arxiv.org/abs/1812.07363 · HTML: https://mlsanity.com/paper/1812.07363/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [Towards Inclusive HRI: Using Sim2Real to Address Underrepresentation in Emotion Expression Recognition](https://mlsanity.com/paper/2208.07472/) (2022)
- [A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection](https://mlsanity.com/paper/2510.14314/) (2025)
- [Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry](https://mlsanity.com/paper/2104.13710/) (2021)
- [Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity](https://mlsanity.com/paper/2602.18525/) (2026)

## Abstract (arXiv)

In this paper, we provide a synthetic data generator methodology with fully controlled, multifaceted variations based on a new 3D face dataset (3DU-Face). We customized synthetic datasets to address specific types of variations (scale, pose, occlusion, blur, etc.), and systematically investigate the influence of different variations on face detection performances. We examine whether and how these factors contribute to better face detection performances. We validate our synthetic data augmentation for different face detectors (Faster RCNN, SSH and HR) on various face datasets (MAFA, UFDD and Wider Face).