An interactive video wall that captures and amplifies people’s characteristic movements, enhancing motion as a form of expression.
Over the past year I have been exploring the idea of movement as a form of expression that’s inherently linked to our identity. It conveys individuality and emotion: we can be identified by our gait, or infer someone’s mood by their pose. I want to maximize this expressiveness potential and explore ways of enhancing people’s movements. I want to foreground motion by separating it from the body, silhouette, features, or clothes, making people rethink how they build their identity and their relation to others.
For this purpose I created a 3-part video wall that analyzes people’s characteristic movements, classifies and amplifies them, enhancing its expressiveness. I used motion capture hardware and machine learning algorithms to showcase expanded forms of motion which feature different textures, forms and colors. Each wall is a single-user experience, and by placing the three walls next to each other, the audience will hopefully reflect and make connections between the repeating patterns that connect them together.