International Catrobat Association

Mentors: Student: Harsh somankar

Currently, Catrobat users have to return to the Catroid IDE every time they want to play their projects, which breaks the experience and makes their creations feel less like real, standalone apps. To address this, I propose introducing a “Pin to Home Screen” feature that allows users to launch their projects directly from the Android home screen, just like any other app. This will be achieved using a lightweight ShortcutTrampolineActivity that retrieves the project ID and opens it directly in Play mode, along with proper task configuration so each project runs independently and exits back to the home screen instead of the IDE. The project will include a smooth UI integration with adaptive icons, a decoupled StageActivity for independent execution, robust lifecycle handling to manage renamed or deleted projects safely, and comprehensive automated testing (JUnit and Espresso) to ensure reliability across different Android versions.

Mentors: Student: siddharth_raj

Cephalopods display complex behavioral states such as stress, curiosity, aggression, and comfort, but interpreting these states currently depends on expert manual observation and lacks scalable, reproducible tools. This project will build an open source multimodal pipeline that automatically infers and classifies cephalopod behavioral sentiment from video and optional bioacoustic data using computer vision and deep learning. The pipeline will include a dataset ingestion and preprocessing module supporting common video and audio formats, a behavioral feature extractor capturing locomotion, skin pattern dynamics driven by chromatophores, and body posture changes, and a sentiment classification system mapping extracted features to labeled behavioral states. A lightweight deployment API and interactive demo dashboard will make the system accessible to researchers and educators. Key deliverables include dataset ingestion and preprocessing scripts, a multimodal model baseline combining video and optional audio streams, a behavioral feature extraction module for movement and color and posture signals, a sentiment label classifier with training and evaluation scripts, full documentation with reproducible experiments, and a REST API with a demo dashboard for inference. The system will be designed for extensibility across species, datasets, and deployment environments including edge devices.

Mentors: Student: Amit Matth

This project aims to complete the transition of Pocket Paint (Paintroid) to Flutter by ensuring compatibility with legacy Kotlin/Java-based .catrobat project files and implementing missing native features in the Flutter version. Currently, older projects cannot be reliably opened, which risks loss of user data and limits adoption of the new app. To solve this, I will design and implement a compatibility layer that parses legacy Kryo-serialized binary data from the native Android application and converts it into the Flutter-compatible JSON-based project structure. The system will follow a structured pipeline (binary decoding → Dart model mapping → JSON serialization → rendering) to accurately reconstruct layers, strokes, and metadata. In addition, I will implement fallback rendering strategies for unsupported tools to preserve visual fidelity and ensure all legacy files remain accessible. Once the compatibility layer is stable, I will focus on implementing selected missing native drawing tools in Flutter to further close the feature gap between the old and new applications. The project will follow a test-driven approach with extensive unit and integration testing using real legacy files to ensure correctness and stability. Deliverables include: - Kryo-to-Dart parsing system for .catrobat files - Fully integrated backward compatibility layer in the Flutter app - Fallback rendering for unsupported legacy features - Comprehensive unit and integration test suite - Documentation for architecture and long-term maintenance - (Stretch goal, if time permits) Implementation of selected high-priority missing native tools (e.g., Fill Tool)

Mentors: Student: Rohan_Sharma_

It is an AI layer that sits on top of a Unity marine ecosystem simulation and translates live simulation data into plain English narration, causal explanations, and natural-language spawn commands all without touching the underlying simulation logic.

Mentors: Student: Shivansh Pachnanda

This project addresses the challenge of bringing 3D-generated marine organisms to life through scalable and automated animation. While recent advances enable the generation of detailed 3D models, these assets often lack realistic motion and require manual rigging and keyframing, limiting their usability in interactive and educational applications. To solve this, the project proposes a skeleton-based procedural animation system built using Python, Blender, and Unity. The system will automatically generate rigs and apply biologically inspired motion using procedural techniques such as kinematic chains and wave-based movement, enabling consistent animation across a wide range of marine organisms. The final deliverables will include a modular animation pipeline, support for multiple movement behaviors (e.g., swimming and idle states), integration with existing 3D generation workflows, and documented demos showcasing real-time visualization in Unity.