AI Neural Painting — Lucas Marques

The Problem

An e-commerce platform offering personalized gifts needed a way to transform customer photos into stylized artwork. The existing manual process was prohibitively slow and expensive, taking approximately 26 hours and $5 per image. The challenge was to automate this workflow completely while maintaining high visual quality.

Automated AI neural painting workflow pipeline — The workflow pipeline demonstrates how AI seamlessly replaces the manual process.

The Engineering

I developed a full automation pipeline using OpenCV, Pillow, and Stylized Neural Painting deployed on AWS. The system utilizes a dual-pathway neural renderer that translates images into paintings using vector representations. It incorporates Optimal Transport Loss to ensure similarity to the input and Differentiable Rendering to fine-tune the artistic output. The pipeline handles everything from face detection and background removal to the final application of oil painting effects.

Local Binary Patterns face detection visualization — Visualization of the Local Binary Patterns (LBP) algorithm used for precise face detection and alignment.

Dual-pathway neural rendering architecture diagram — The dual-pathway neural rendering architecture that transforms vector representations into stylized artistic strokes.

The Results

The automation achieved a 100x increase in performance, reducing processing time from 26 hours to just 15 minutes per image. Cost efficiency improved dramatically, dropping from $5 to $0.25 per image (20x reduction). This scalability allowed the store to handle higher order volumes without sacrificing quality or delivery speed.

Input customer photograph before neural painting — Input image.

Output oil painting generated by neural rendering — Output image.