Image Reconstruction of Tablet Front Camera Recordings in Educational Settings

Jul 10, 2020·
Dr. Rafael Wampfler
Rafael Wampfler
,
A. Emch
,
B. Solenthaler
,
M. Gross
Abstract
Front-facing cameras on tablets used in educational settings can capture rich information about student engagement and affective states, but raw video recordings raise significant privacy concerns and require large storage. We present a method for reconstructing informative visual representations from compressed or degraded tablet front camera recordings in educational contexts, enabling engagement analysis while reducing privacy and storage overhead. The approach leverages image reconstruction neural networks to recover facial and behavioral features sufficient for downstream affect and engagement modeling.
Type
Publication
In Proceedings of the International Conference on Educational Data Mining (EDM), Virtual
publications
Educational Data Mining Computer Vision Engagement Detection Image Reconstruction
Dr. Rafael Wampfler
Authors
Dr. Rafael Wampfler
Senior Researcher & Lecturer

I am a Senior Researcher & Lecturer at the Computer Graphics Laboratory of ETH Zurich, and a Research Consultant at Disney Research. I lead the Digital Character AI team at CGL and, as PI of an SNF Ambizione grant, a line of generative models that produce expressive 3D facial animation from speech and text under explicit control of emotion and expression. My research interests include conversational digital characters, generative animation, affective computing, human-computer interaction, and applied machine learning.

My vision is to create intelligent digital humans that can naturally communicate, understand, and support people across domains such as education and mental health. My research focuses on multimodal artificial intelligence for interactive digital humans, developing models that combine large language models, affective computing, and data-driven animation to create embodied conversational agents endowed with autonomous agency, consistent values, and beliefs.

My work bridges machine learning, human–computer interaction, and computer graphics to enable AI systems such as Digital Einstein and interactive patient avatars for psychotherapy training and health education.