Christian Holz
Papers
4
Total Citations
28
H-Index
4
About
Christian Holz is a leading researcher at the intersection of robotics, human-computer interaction, and privacy-preserving sensing. His early work focused on advancing the control of complex robotic systems, particularly parallel manipulators, where he developed time-efficient methodologies for direct dynamics identification—enabling accurate model-based feedforward control without costly parameterization. This foundational contribution, cited over a dozen times, remains relevant for modern robotic applications requiring precise, decoupled control. More recently, Holz has pioneered novel approaches to ego-motion estimation and egocentric perception, notably introducing privacy-preserving techniques that leverage extremely low-resolution cameras for visual-inertial odometry—a critical step toward trustworthy wearable and mobile devices. His 2020 paper on this topic has garnered attention for addressing growing privacy concerns in AR/VR. In 2025, Holz released EgoPressure, a comprehensive dataset for hand pressure and pose estimation from an egocentric view, enabling new research into touch interaction understanding for mixed reality and robotics. With over 28 total citations across his most influential works, Holz continues to shape how machines perceive and interact with humans, balancing performance with ethical considerations in sensing technology.
Research Focus
Key Achievements
Top Papers
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