Interactive Art Research

Designing immersive digital experiences that respond to user behavior through AI and real-time interactivity. This research focuses on creating dynamic installations that transform environments through generative art.

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Objective

This research explores how real-time user input can dynamically influence generative art through machine learning models. The system enables installations that evolve based on audience interaction, creating unique experiences for each participant.

Key Technologies

• Neural networks for real-time generation
• WebGPU accelerated rendering
• Motion sensor integration
• WebAudio API for sound spatialization

Features

• Multi-user gesture recognition
• Environment-aware lighting
• Procedural soundscapes
• Adaptive visual complexity

Art Installation Simulator

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User Count

Requires WebGPU enabled browser for optimal interaction

Art Research

Whitepaper

Technical exploration of interaction models in art installations with performance benchmarks across different input modalities

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Codebase

Open-source implementation including machine learning models, shader libraries, and sensor input frameworks for interactive environments

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Exhibit Guide

Interactive web application demonstrating our core research in digital art through live interaction samples without requiring special hardware

Open Interactive Guide