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A deep-learning framework for testing the correctness of the visualizations rendered in games

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Automated Visual Anomaly Detection Framework

This repository provides a complete pipeline for generating, training, and deploying automated visual anomaly detection in real-time 3D applications (e.g., video games). It covers both texture and mesh anomaly detection through two-stage deep learning architectures, as well as Unity-based data generation tools and deployment artifacts.

It is a continuations of the two papers:

Project Overview

Getting Started

  1. Data Generation

  2. Training & Inference

  3. Deployment


For detailed information on each component, click the links above.

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A deep-learning framework for testing the correctness of the visualizations rendered in games

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