# A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines

arXiv: https://arxiv.org/abs/1808.02595 · HTML: https://mlsanity.com/paper/1808.02595/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation](https://mlsanity.com/paper/2211.17235/) (2022)
- [SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction](https://mlsanity.com/paper/2108.09593/) (2021)
- [Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data](https://mlsanity.com/paper/2105.10837/) (2021)
- [Graph-Guided Dual-Level Augmentation for 3D Scene Segmentation](https://mlsanity.com/paper/2507.22668/) (2025)

## Abstract (arXiv)

Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. small data), such as human pose and behavior estimation/tracking which could be highly personalized. In this paper, we present a semi-supervised data augmentation approach that can synthesize large scale labeled training datasets using 3D graphical engines based on a physically-valid low dimensional pose descriptor. To evaluate the performance of our synthesized datasets in training deep learning-based models, we generated a large synthetic human pose dataset, called ScanAva using 3D scans of only 7 individuals based on our proposed augmentation approach. A state-of-the-art human pose estimation deep learning model then was trained from scratch using our ScanAva dataset and could achieve the pose estimation accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain adaptation on the synthetic images, in which its pose estimation accuracy was comparable to the same model trained on large scale pose data from real humans such as MPII dataset and much higher than the model trained on other synthetic human dataset such as SURREAL.