Vectorizer

Engineering Tutorial
1 credits / execution

Convert raster images to scalable SVG vectors

Technical Overview

Transform low-fidelity raster data into atomic, scalable SVG assets with deep neural precision.

Unlike legacy trace tools that produce redundant path data, our implementation utilizes perceptual neural networks to determine optimal bezier curvatures, resulting in minimized file sizes and professional-grade editability within the Figma property panel.

"The Vectorizer implementation is optimized for design system scale, ensuring perfect node density and clean attribute injection."

Core Capabilities

🎯

Atomic Precision

Maintains high-fidelity edge detection while converting pixel data to mathematical paths.

Optimized Nodes

Advanced curvature simplification algorithms reduce unnecessary anchor points for lighter SVG data.

📐

Infinite Scalability

Outputs resolution-independent vector graphics, compatible with all production environments.

🎨

Figma Integration

Injects vector data directly into your active layer stack with preserved color spaces.

Execution Protocol

01

Initialize Asset

Select your source raster layer (PNG, JPG, or WEBP) on the Figma canvas.

02

Technical Preview

Trigger the Vectorizer module and initiate a neural analysis of the pixel density.

03

Neural Synthesis

The engine generates optimized path structures with automated bezier refinement.

04

Production Injection

Confirm the trace to inject the finalized SVG layers directly into your workspace.