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Gesture Canvas AI

Next-Generation Browser Digital Art Canvas Controlled via Real-Time Hand Tracking

A touchless web canvas utilizing MediaPipe and TensorFlow.js for real-time 21-point hand landmark detection, enabling users to draw, erase, switch palettes, and manipulate shapes in mid-air through webcams.

Client / Entity
Proprietary Computer Vision Project
Role
Computer Vision & Creative Frontend Developer
Timeline
2026-04
Live Links

Interactive Live Web Sandbox

Interact with the live production deployment directly below across Desktop, Tablet, and Mobile viewports.

Open in Full Browser
https://gesture-canvas.jaggu.me

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Measurable Impact & Benchmarks

Real performance data and business outcomes achieved for Proprietary Computer Vision Project.

60 FPS

Frame Rate

Smooth real-time gesture tracking in standard browsers

14ms

Inference Latency

Ultra-low client-side hand landmark tracking delay

100% Client-Side

User Privacy

No camera video ever leaves the user’s device

The Challenge

Core Problem & Bottlenecks

Running heavy machine learning hand tracking in client browsers without lag, frame drops, or requiring external software installations.

Engineering Solution

Technical Architecture & Delivery

Engineered WebGL-accelerated canvas pipelines and optimized MediaPipe hand landmark tracking running at a silky smooth 60 FPS directly in the browser.

Key Functional Capabilities

Key features and engineering highlights implemented

Real-Time 21-Point Hand Landmark Tracking via browser webcam

Pinch-to-Draw, Palm-to-Erase, and Peace-Sign-to-Palette gesture controls

Hardware-accelerated HTML5 Canvas rendering at solid 60 FPS

High-Resolution Artwork Export (PNG/SVG) with instant social sharing

Zero server latency — 100% private client-side execution

Technology & Infrastructure

Modern frameworks and tools selected for this architecture

Frontend & UI

ReactTypeScriptHTML5 Canvas APITailwind CSS

Backend & APIs

MediaPipe Hands MLTensorFlow.js WebGL backend

Tools & Infrastructure

ViteVercel

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