Neural Style Transfer Research

NST

Neural Style Transfer Research

Exploring real-time style transfer algorithms using generative neural networks for dynamic art composition. This project combines deep learning techniques with creative applications in digital canvas rendering.

Objective

This research investigates real-time neural style transfer implementations using TensorFlow.js, focusing on low-latency canvas rendering for interactive art installations. The system enables users to apply artistic styles to live camera feeds or uploaded images with sub-second processing.

Key Technologies

• TensorFlow.js for browser-based inference
• WebGPU accelerated neural processing
• Real-time camera stream integration

Features

• 60fps real-time style transfer
• Style blending controls
• Multi-layer neural processing

Style Transfer Demo

Select Image

Select Style

Note: This is a conceptual interface. Actual implementation requires WebGL support.

Publication Details

Whitepaper

Technical exploration of style transfer optimization techniques with performance benchmarks comparing different convolutional network architectures.

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Codebase

Open-source implementation including trained models, WebAssembly optimizations, and shader code for GPU accelerated computation.

View on GitHub

Demo

Interactive web application demonstrating real-time style transfer on live video input without external dependencies.

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