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AI-Powered 3D Hair Analysis Using Multi-View Image

AI-Powered 3D Hair Analysis Using Multi-View Image
Category:
AI / FMCG Beauty Care

The Problem Statement

Accurately analysing hair characteristics such as volume, fizziness, and curliness is essential in the beauty, cosmetics, and dermatology industries. Traditional hair analysis methods rely on manual assessments, photogrammetry, or physical measurements, which are time-consuming, expensive, and subjective. To overcome these limitations, we developed a computer vision and deep learning solution that utilizes multi-view input images to reconstruct a detailed 3D hair model for precise analysis. 

The Solution

Our AI-powered pipeline reconstructs 3D hair models from multiple images and extracts key parameters using deep learning. The process involves: 

  • Multi-View Image Acquisition: Capturing a high-resolution monocular video by rotating the camera in a 360-degree manner around the stationary subject, focusing on the subject’s hair at different angles. 
  • Hair Segmentation & Alpha Matting: Extracting fine hair strands for each view. 
  • 3D Hair Reconstruction: Using multi-view stereo and deep learning to build a 3D hair strand model. 
  • Volume Estimation: Computing bulk hair volume and frizzy hair volume from 3D hair geometry. 
  • Fizziness Analysis: Measuring strand deviation and irregularities using a 3D hair strand model. 
  • Curliness Analysis: Calculating wave frequency and curvature using 3D hair strands. 
List of processes to reconstructs 3D hair models from multiple images and extracts key parameters using deep learning

Features of the Solution:

  • Multi-View Image Acquisition 
    • Capture a video from a camera rotating in 360 degrees around the stationary subject sitting on a chair under uniform lighting and some variation in the background. 
    • Uses camera pose estimation to align images for 3D reconstruction. 
    • Data can be captured using regular cameras, mobile phones, or depth sensors. 
  • Hair Segmentation 
    • Bulk Hair Segmentation: A deep learning model detects the main hair region. 
    • Multi-View Consistency: Matches hair details across multiple images to preserve structure. 
    • Output: Clean hair masks for each input image, isolating fine strands. 
  • 3D Hair Reconstruction from multi-view images 
    • Structure-from-Motion (SFM): Extracts camera positions and aligns images. 
    • Multi-View Stereo method generates a dense point cloud of hair structure, while a NeRF deep learning model refines the 3D hair model. 
    • In the output, we get a high-fidelity 3D hair strand model with precise details of every hair strand. 
  • 3D Hair Volume Estimation 
    • Voxelization & Convex Hull methods convert the 3D hair model into volumetric data. 
    • 3D Point Cloud processing computes the total occupied space by the hair, which also helps in computing the 3D bulk hair volume. 
    • Perform frizzy hair detection from 3D point cloud data for estimating frizzy hair volume 
  • Fizziness Score Calculation from 3D Hair Strands 
    • Perform stray strand analysis to identify the frizzy hair regions. 
    • Statistical Variability: Compares strand angles to smooth reference hair to determine frizz level. 
    • In the output we get fizziness heatmap and corresponding numerical score. 
  • Curliness Score Calculation from 3D Hair Strands 
    • Measures strand curvature in 3D space using Bezier fitting and categorize the hair into straight, wavy, curly, or coily. 
    • Curliness Score (0-10 scale) given as follows: 
      • 0-2 (Straight Hair) – Minimal curvature. 
      • 3-5 (Wavy Hair) – Loose, gentle waves. 
      • 6-8 (Curly Hair) – Defined curls. 
      • 9-10 (Coily Hair) – Tight, spring-like curls. 
    • We get curliness heatmap and classification as the output. 

Visualization

  • Hair Mask & Hair Strand Overlay – Displays segmented hair and individual hair strands across views. 
  • 3D Hair Model Viewer – Interactive rotation and zoom functionality. 
  • Heatmaps – Shows frizz and curl distribution. 
  • Volume & Density Charts – Graphical representation of hair volume. 

Optimization & Porting

  • Efficient Multi-View Matching: Uses feature-matching networks for faster processing. 
  • Adaptive Mesh Resolution: Balances detail vs. computation speed. 
  • Parallel Processing: Uses GPU acceleration for fast 3D reconstruction and matting. 
  • Developed Desktop Application for Professionals – PyTorch-based high-resolution 3D hair strand modelling. 
Precise and Automated 3D Hair Analysis for hair volume, fizziness score and curliness score.

Benefits Delivered

  • Precise and Automated 3D Hair Analysis for hair volume, fizziness score and curliness score. 
  • Recommends shampoos, conditioners, and treatments based on hair characteristics. 
  • Provides curl and frizz control suggestions based on scores. 
  • Works with regular smartphone cameras, making advanced hair analysis widely accessible. 
  • Dermatology: Aids in diagnosing hair disorders like frizz, thinning, and scalp health. 
  • Virtual Try-On & AR: Supports 3D hair styling applications for e-commerce.