Skylar Ziyi Wang

Multidisciplinary Designer • Technologist
Skylar (Ziyi) Wang is a designer and technologist based in New York and Shanghai.

She weaves the untranslatable into playful interactions through graphics, code, and fabrications.
Thesis Faculty
Ever BusseyAndrew ZornozaJesse HardingFran Hoepfner

Body, Movement, and Translation

“Body, Movement, and Translation” is an interactive digital installation that delves into the realm of the inexpressible—those visceral feelings that elude verbal articulation. This experiential interactive work invites participants to engage with a curated archive of gestures, each a unique interpretation of a range of gestures experienced by the artist herself. By mirroring these movements, visitors not only encounter a dynamic visualization but also uncover layers of meaning—revealing where I physically harbors these emotions, and the form they take through shape, color, texture, and tempo.

The participants will be presented with a set of physical cards, each illustrated with a specific gesture that corresponds to one of the emotions. These cards serve not only as a guide but also as an invitation for the audience to physically immerse themselves in the experience.

Physical Guiding Cards

Participants will select a card of their choice and then mimic the gesture depicted. This act of embodiment allows them to connect personally with the emotional intent behind the gesture. Once a gesture is performed, it triggers a corresponding visualization on the screen. These visualizations are uniquely crafted to represent the emotional essence of each gesture, enhancing the experience with synchronized soundscapes that further encapsulate the mood and tone of the emotion being explored.

Discomfort Visualization
Comfort Visualization

This project also highlights the discrepancy between human and machine perception. For example, while a human observer can easily distinguish between nuanced expressions and postures, the machine, limited by its 31-point model, often struggles to discern subtle differences. This challenge underscores a fascinating tension: at times, machine learning approaches can seem remarkably close to human-like understanding, yet there remain significant gaps where the complexity of human experience is lost or oversimplified.

Human View vs. Machine View