Glyph-Based Visualization of Affective States

May 25, 2020·
N. Kovačević
Dr. Rafael Wampfler
Rafael Wampfler
,
B. Solenthaler
,
M. Gross
,
T. Günther
Abstract
We present a glyph-based visualization technique for representing affective states derived from multimodal sensor data. Individual affective states — characterized by dimensions such as valence, arousal, and discrete emotions — are encoded as compact visual glyphs designed for intuitive interpretation and scalable display. The glyph design leverages established perceptual principles to map affective dimensions to visual channels including shape, color, and size. We evaluate the visualization through user studies demonstrating that glyph-based representations enable faster and more accurate interpretation of affective patterns compared to standard chart-based displays.
Type
Publication
In Eurographics/IEEE VGTC Symposium on Visualization (EuroVis), Virtual
publications
Affective Computing Visualization Glyph Design Information Visualization
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.