Research Log

Eman demos a website that reads grid-based inputs and game elements, helping AI reliably understand board game layouts, mechanics, and rules.

Zeineb introduces her medical AI project focused on early brain tumor detection, explaining how AI, precision, and analysis can improve patient outcomes.

This session breaks down Apple’s 3D reconstruction approach, exploring its design choices and how the tools can be applied or extended in future projects.

Zeineb shares progress on her model, discussing recent experiments and how she’s refining the system while evaluating results as the project moves forward.

Mugi updates her project by moving from a single-camera setup to a dual-camera solution, improving motion capture accuracy and overall system reliability

Eman shares updates to his robotics project, improving the camera and robot setup in Isaac Lab to create a more stable and accurate system for future autonomy.

Eman showcases a major robotics update, where the robot now navigates autonomously toward rewards with high accuracy based on its environment.

Check out Muhammad Eman Aftab’s work from our Budapest research lab, where he builds AI projects in public, sharing progress, challenges, and learnings.

Kevin introduces Cenarius, a project focused on scanning real-world environments like farmlands and construction sites to create interactive 3D digital spaces.

Kevin compares different AI agents for image analysis, showing how each approaches visual tasks and where certain tools perform better by use case.

Eman advances his vision-based system by upgrading camera calibration from 2D to 3D and setting up the Isaac environment for testing and future autonomy.

David showcases Venato, an AI-powered regulation monitoring platform, explaining how it uses AI to collect, structure, and surface regulatory information.

Munkhchimeg Sergelen explains how SignMate avatars are created and why clean, precise motion data is essential for accurate ASL handshapes and movements.

Mugi tests Plask and FreeMoCap for ASL motion capture, comparing how well they track gestures and outlining strengths, limits, and animation challenges.

Mugi shares early SignMate experiments capturing ASL motion, testing MediaPipe, RPM, and ASL Mocap datasets to evaluate gesture accuracy and complexity.

Mugi explains how SignMate converts ASL gloss into 3D avatar motion, walking through the system that maps language inputs to realistic signing gestures.

Munkhchimeg Sergelen explains how ASL differs from spoken English and why these differences are essential when designing SignMate.

Zeineb explains why she upgraded her MRI tumor-detection model from ResNet18 to ResNet50 to build a stronger system for more accurate detection.

Eman introduces his Lightbloom AI Lab project: a system designed to turn simple hand-drawn sketches into fully playable board games.

Kevin showcases a breakthrough in 3D reconstruction using Gaussian Splatting, enabling high-quality 3D scans without expensive computing or specialized hardware

Eman demos a vision-driven robot control system that reads printed signs to interpret commands and move autonomously in real time

Join us inside the Lightbloom Lab in Budapest as Mugi walks through her latest experiment attempting to adapt our teammate Kevin into a real-time signing avatar

Kevin demonstrates how to use the Lightbloom Lab’s newly assembled computer cluster, the shared infrastructure powering all ongoing projects

Join us inside the Lightbloom AI Lab in Budapest as our researcher, Eman, demonstrates the latest updates to our Sketch-to-Game Generator.

Zeineb demonstrates the latest improvements to the Autoscan AI model, advancing early brain tumor prediction at the Lightbloom AI Lab in Budapest.

Meet Mugi, an AI researcher in our Budapest Lab, building SignMate to make communication more accessible through speech-to-sign avatars.