Institute for Intelligent Interactive Systems
Digital capture of human bodies is a rapidly growing research area in computer vision and computer graphics that puts scenarios such as life-like mixed-reality (MR) virtual-social interactions into reach. Therefore, we offer projects for modeling and capturing humans at the intersection of computer vision, computer graphics, and machine learning.
- Computer Graphics, Computer Vision, Virtual Reality and Related Simulation
- CLS Student Project (MPG ETH CLS), ETH Zurich (ETHZ), Master Thesis, Semester Project
Motion modeling, for robots and humans alike, has become a highly active field of research and developed methods have found many applications in state estimation, navigation, localization, self-driving cars, drones and autonomous aerial vehicles, activity detection, pose estimation, or even rehabilitation. Sensors often include cameras as well as inertial motion units, such as accelerometers and gyroscopes, which are now commonly embedded in nearly each device today, from heavy machinery in industrial settings to personal devices, such as tables, phones, and watches.
However, the lack of large-scale, labeled datasets impedes progress in developing robust and generalized predictive models for neural inertial-based motion estimation. Especially, labeled data in human activity data is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone.
- Aerodynamics, Computer Vision, Flight Control Systems, Flight Dynamics, Intelligent Robotics, Modeling and Simulation, Robotics and Mechatronics, Simulation and Modelling, Virtual Reality and Related Simulation
- ETH Zurich (ETHZ), Master Thesis, Semester Project
How do we design differentiable methods for full-body pose estimation from sparse sensors to create physically plausible results?
- Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project