Mentor: Yuzhi Lai

Email: yuzhi.laispam prevention@uni-tuebingen.de

Traditional interaction methods such as gesture and voice commands present significant challenges. Gesture-based interactions require users to perform precise movements within the field of view of a camera, restricting their mobility and range of interaction. On the other hand, speech-based commands can be ambiguous, as verbal descriptions often lack the spatial specificity needed for accurate robotic response. In contrast, gaze-based interaction offers a silent, unobtrusive, and intuitive alternative that enables hands-free control. By leveraging AI-driven eye-tracking technology, we aim to develop a gaze-based control system that enhances accessibility and interaction efficiency, providing a versatile solution for users in both general and specialized settings.

This project aims to develop a gaze-driven dialogue system for ARIA glasses that allows users to form and communicate simple sentences using only eye movements. Unlike existing assistive communication tools that rely on manual input or predefined selection grids or GUIs, this system will leverage real-time eye-tracking to provide an intuitive way for users to collaborate with robots. This project aims to provide a voice-and hands-free communication solution that significantly enhances the quality of life for individuals with severe motor and speech impairments.

 

Mentor: Rafia Rahim

Email:  rafia.rahimspam prevention@uni-tuebingen.de

Recent advances in stereo vision have led to the emergence of "foundation" stereo networks—such as FoundationStereo, Stereo Anything, and MonSter—which demonstrate robust generalization and adaptability across diverse domains and scene types. However, these state-of-the-art models are typically very large, computationally intensive, and often impractical for deployment on edge devices or in latency-sensitive applications. This thesis investigates the use of knowledge distillation to compress these foundation models into lightweight, task-agnostic student networks, aiming to preserve their broad generalization and high performance while drastically reducing computational requirements. The research will explore multi-teacher distillation, where a student model learns from multiple foundation networks simultaneously, as well as specialized distillation losses tailored to stereo depth estimation (e.g., disparity-aware and confidence-aware losses). The thesis will benchmark distilled student models on a range of stereo datasets (KITTI, Middlebury, ETH3D, indoor/outdoor datasets) and assess performance not just on accuracy and runtime, but also on cross-domain robustness, thereby demonstrating the practical benefits of distilled foundation stereo models.

Requirements: good programming skills, deep learning knowledge.