# G2D: from GTA to Data

arXiv: https://arxiv.org/abs/1806.07381 · HTML: https://mlsanity.com/paper/1806.07381/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution](https://mlsanity.com/paper/2308.01738/) (2023)
- [High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation](https://mlsanity.com/paper/2305.01732/) (2023)
- [Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond](https://mlsanity.com/paper/2202.14034/) (2022)
- [GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal](https://mlsanity.com/paper/2210.04708/) (2022)
- [Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic](https://mlsanity.com/paper/2310.04416/) (2023)
- [Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images](https://mlsanity.com/paper/2306.01938/) (2023)
- [OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision](https://mlsanity.com/paper/2401.17061/) (2024)
- [From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations](https://mlsanity.com/paper/2502.12303/) (2025)

## Abstract (arXiv)

This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city. The target users of our software are computer vision researchers who wish to collect hyper-realistic computer-generated imagery of a city from the street level, under controlled 6DOF camera poses and varying environmental conditions (weather, season, time of day, traffic density, etc.). G2D accesses/calls the native functions of the game; hence users can directly interact with G2D while playing the game. Specifically, G2D enables users to manipulate conditions of the virtual environment on the fly, while the gameplay camera is set to automatically retrace a predetermined 6DOF camera pose trajectory within the game coordinate system. Concurrently, automatic screen capture is executed while the virtual environment is being explored. G2D and its source code are publicly available at https://goo.gl/SS7fS6 In addition, we demonstrate an application of G2D to generate a large-scale dataset with groundtruth camera poses for testing structure-from-motion (SfM) algorithms. The dataset and generated 3D point clouds are also made available at https://goo.gl/DNzxHx