Anomaly detection plays a critical role in surveillance systems,particularly in the automation of large-scale monitoring scenarios. Anomaly detection algorithms require datasets comprisedof large amounts of annotated data to train and evaluate models. Gathering and annotating this data is a labour intensivetask, that can be costly if outsourced to external partners. Amethod to get large amounts of annotated data explored bycurrent state-of-the-art research is to generate it artificiallyusing 3D applications, which has the advantage of being ableto generate new frames in quick succession. This poses newissues for the end-user by being a specialized field, whichmeans low-expertise users rely on external partnerships tohave this option. In this paper we propose an application thatsynthesizes datasets using 3D models and simulates anomalieson real backgrounds using the Unity Engine. Additionally, weintroduce a high-usability User Interface attached to a highlycustomizable simulation that simplifies the process of generating synthetic data without the need for specialized expertise in3D animation. Testing datasets augmented with synthetic datamade using our application gave promising results, with increases in both AUC and F1 scores in all cases. This indicatesthat synthetic data generation for low-expertise end users is aviable approach, and we recommend future works to focus oncreating high variation in their data and to use photorealistic3D models and lighting.