Teaching & theses

In the fall term 2026, we are teaching:

• Human-Computer Interaction 2026 bachelor course
• Mixed Reality 2026 master course

Available student thesis projects

8 projects are available (via 5 master’s theses and 5 semester theses). Currently, all bachelor’s theses have been assigned. The list below is up-to-date.

Please submit your CV and transcripts through SiROP. In case of questions, please contact us via SiROP.

• Learning from Cardiovascular Signals During Sleep

  The activity of the cardiovascular system during sleep carries vital information about a person's overall health. The exact ways in which the heart and brain interact during sleep to influence long-term disease risk remain poorly understood. Using wearable sensing across multiple data sources, we aim to uncover these hidden dynamics. We are looking for students who are passionate about signal processing, machine learning, and statistical analysis of medical data.

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  • contact: Max Möbus
  • semester project

  • Signals
  • Wearable Sensors
  • Machine Learning
  • Cardiovascular Risk Factors
  • Long-term Health

• Motion Capture Data Collection for Benchmarking Deep Learning-Based Pose Estimation from Sparse IMUs

  Deep learning-based human pose estimation from a small number of body-worn inertial measurement units (IMUs) is crucial for practical AR/VR and healthcare applications. However, most existing datasets are collected under laboratory conditions with high-end hardware and carefully calibrated, perfectly placed sensors. As a result, they implicitly assume near-ideal sensor behavior and mounting, which does not reflect the noisy, misaligned, and drift-prone data typically produced by affordable commodity IMU devices in real-world use.
  This project focuses on recording a new motion dataset that explicitly captures these real-world imperfections. The goals include:
  - Collecting synchronized IMU and reference motion capture data across a diverse set of activities
  - Running a user study to collect high-quality, systematically recorded IMU data from non-specialized IMU devices across a diverse group of participants
  - Preprocessing and packaging the recordings for seamless integration with an existing benchmarking platform

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  • contact: Dominik Hollidt
  • semester project

  • pose estimation
  • deep learning
  • machine learning
  • computer vision
  • benchmark
  • dataset

• Synthesizing Avatar Motion from AI Glasses (in collaboration with Google XR)

  We will generate expressive avatar motions from lightweight AI Glasses observations (where the wearer’s body is almost never visible). Using sparse inputs alone, such as speech audio, head pose, IMU signals, and occasional observations of the hands, our method will generate plausible, temporally coherent, and speech-synchronized torso, shoulder, and arm motion for telepresence and embodied avatar applications.

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  • contact: Christian Holz, Dominik Hollidt
  • master's thesis

  • Egocentric vision
  • human motion generation
  • diffusion models
  • flow matching
  • multimodal learning
  • AI glasses
  • avatars

• Multi-View 3D Hand Pose Estimation from Dynamic Egocentric Captures

  This project explores multi-view 3D hand pose estimation by leveraging several dynamically moving viewpoints to address the persistent challenges of occlusion, viewpoint ambiguity, and fine-grained articulation. The goal is to develop a modern multi-view fusion model that exploits cross-view geometric consistency and spatio-temporal cues to achieve robust hand pose estimation. Beneficial will be our access to a motion simulation engine capable of generating large-scale synthetic multi-camera datasets. This will enable experimentation with synthetic-to-real transfer and state-of-the-art training strategies.

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  • contact: Dominik Hollidt
  • semester project
  • master's thesis

  • Computer Vision
  • 3D Hand Pose Estimation
  • Multi-Camera
  • Egocentric Vision
  • Multi-View Fusion

• Learning Interaction-Aware Robot Hand Retargeting from monocular RGB videos

  Human hand demonstrations provide useful training data for dexterous robots. However, directly mapping human hand motion to a robot hand often produces unstable contact, object drops, or incorrect object motion.
  In this Master’s thesis, you will train a temporal model to improve robot hand controls using corrections generated by an interaction-based teacher. The teacher uses the object mesh during training, but the learned model will only use observed hand and object motion during inference. The project will test whether this approach can improve manipulation success while avoiding costly physics optimization for every new demonstration.

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  • contact: Berken Demirel
  • master's thesis

  • Dexterous manipulation
  • Interaction
  • hand retargeting
  • imitation learning
  • temporal models
  • human demonstrations
  • object manipulation

• Multi-Modal Spatial Reasoning with Human Motion

  Multi-modal language models still struggle with the 3D world — judging directions, taking another viewpoint, estimating distances — and prior work traces most of these failures to egocentric–allocentric frame transformation, not perception. Humans learn this by moving through space. This thesis will test whether full-body human motion can supply that signal. The plan is to extract a spatial-reasoning dataset coupled to real human motion, train an MLLM on it, and measure whether the learned ability transfers to existing benchmarks.

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  • contact: Dominik Hollidt
  • semester project
  • master's thesis

  • spatial reasoning
  • MLLM
  • VLM
  • egocentric video
  • human motion
  • embodied AI
  • proprioception
  • instruction tuning
  • benchmark

• Motion Capture Data Collection for Robotics Retargeting and Sparse IMU-Based Human Motion Estimation

  This project will collect a realistic motion-capture dataset for sparse wearable IMUs, targeting applications in human pose estimation and robotics motion retargeting. Unlike existing laboratory datasets, it will capture real-world effects such as sensor noise, drift, misalignment, and imperfect placement using affordable commodity hardware.

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  • contact: Dominik Hollidt
  • semester project

  • motion capture
  • robotics
  • retargeting
  • motion
  • IMU
  • inertial sensing
  • pose estimation
  • deep learning
  • machine learning
  • computer vision
  • benchmark
  • dataset

• Supervised Contrastive Learning for Cross-Sensor Tactile Representation Learning in Robotics

  Tactile sensors help robots understand contact, force, slip, texture, and objects. However, different sensor types produce very different data, which makes it difficult to train models that work across sensors.
  In this Master’s thesis, you will develop a learning method that combines self-supervised learning with sparse labels such as force, motion direction, and indenter type. The goal is to learn useful tactile representations that transfer to new sensors and physical conditions.

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  • contact: Berken Demirel
  • master's thesis

  • Tactile sensing
  • representation learning
  • self-supervised learning
  • supervised contrastive learning
  • robotics.

Current thesis students

• Nikola Staykov master's ETH Robotics, D-MAVT, RSC
• Maxime Paschoud master's ETH Data Science, D-INFK
• Yuchen Qin master's UZH Computer Science
• Tong Su master's UZH Computer Science
• Le Qian master's UZH Computer Science
• Qichen Zhao master's UZH, Computer Science
• Baudoin Coispeau semester ETH Robotics, D-MAVT, RSC
• Yu-Ming Chen semester ETH Computer Science, D-INFK
• Monica De Alvaro Mena semester ETH Data Science, D-INFK
• Arif Erdem Akalin bachelor's ETH Data Science, D-INFK
• Sascha Gujer bachelor's ETH Data Science, D-INFK

Past Courses and Seminars

• Mobile Health and Activity Monitoring master course
  • 2026
  • 2025
  • 2024
  • 2023
  • 2022
• Human-Computer Interaction bachelor course
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
• Mixed Reality master course
  • 2025
  • 2024
  • 2023
• Ubiquitous Computing master course
  • 2021
  • 2020
• Adaptive User Interfaces through Machine Learning bachelor seminar
  • 2026
  • 2025
• Egocentric Computer Vision: Perception and Understanding from the First-Person View master seminar
  • 2026
• Reinforcement Learning for Understanding and Modeling Human Behavior master seminar
  • 2026
  • 2025
• Egocentric Perception for Human-World Understanding master seminar
  • 2025
• Advanced Topics in Mixed Reality bachelor seminar
  • 2024
  • 2023
  • 2022
• Advanced Topics in Technical Human-Computer Interaction bachelor seminar
  • 2021
  • 2020