Hi, my name is Ivar Gavelin
MSc in Media Technology, AI and Engineering.

Simulated Swedish cities by combining GIS data and procedural rules in CityEngine and Unreal. Balanced realism and performance by creating a scalable system with modular CGA and custom assets. The code is not public due to collaboration with FOI, but the report is available.

As founder of Aerocapture Productions, I’ve built websites, handled media production, and delivered visual content for real clients. I’ve solved creative and technical challenges using tools like HTML, CSS, JavaScript, and Adobe tools and always adapting to each project's needs.

A survival shooter game where players fight against waves of enemies in an arena. Players can choose between different weapons and abilities, and the game features a variety of enemy types. Developed using Unreal Engine 5.4 and C++, it showcases my skills in game development, including gameplay mechanics, AI behavior, and level design.

My advanced enemy AI system for Arena of Oblivion uses Unreal Engine's Behavior Trees and C++ to create dynamic and challenging enemies. It features perception systems, complex decision-making, and adaptive behaviors that respond to player actions, enhancing gameplay immersion.

An advanced social media website for course reviews, built with React, Node.js, and MongoDB. It features user authentication, course reviews, ratings, and a responsive design. The project demonstrates my skills in full-stack development, including front-end and back-end integration, database management, and user interface design.

Developed a VR-based training game designed to motivate physical activity through gamification. Players fight enemies using full-body movements in rhythm-based waves. Features include real-time pulse tracking, scoreboards, in-game purchases, and dynamic music. The game was tested on real users with positive results on both engagement and physical exertion.

Explored image reproduction by recreating original photos using a dataset of smaller images. Developed Python scripts to optimize the dataset based on color similarity and evaluated visual quality using SNR, SSIM, and S-CIELAB metrics. The project balanced objective metrics with subjective visual appeal.