Human Digital Twin Technology for Smart Virtual Fitting

Accurate garment sizing remains one of the biggest challenges in online and in-store apparel retail.

Key challenges included:

• Most body scanning solutions rely on standard cameras that capture only 2D silhouettes, leading to inaccurate circumference measurements and unreliable fit predictions.
• Manual measurement methods or user-entered data provide only limited linear measurements and fail to capture body geometry, posture, and body mass distribution.
• Retailers face inventory inefficiencies due to inaccurate size selection and poor demand forecasting across size ranges.
• High return rates in eCommerce are frequently caused by sizing mismatches.

These issues reduce customer satisfaction and increase operational costs for fashion retailers.

• LiDAR-Based 3D Body Reconstruction Pipeline – Dynamisch engineered multi-sensor scanning rig built around Intel RealSense L515 LiDAR sensors captured dense depth data from multiple viewpoints. The captured point clouds were fused into a watertight 3D body mesh using geometric processing pipelines implemented with Open3D.

• Parametric Human Digital Twin Generation – The reconstructed mesh was fitted to a parametric human body model to generate a normalized digital twin representation. This enabled extraction of over 40+ anthropometric measurements, including circumferences, limb proportions, and posture-aware metrics.

• ML-Based Fit Prediction – We built ML pipeline built using PyTorch learned the relationship between body geometry features and garment size charts across partner brands. The model predicts best-fit sizes by considering garment tolerances, stretch properties, and cut patterns.

• Real-Time Visualization and AR Fit Simulation – 3D visualization interface built in Unity rendered the user’s digital twin and enabled real-time garment fit visualization for eCommerce and in-store kiosks.

• Privacy-Preserving Measurement System – The scanning pipeline processed only depth and skeletal geometry data, ensuring no identifiable photos or videos were stored. Only anonymized measurement vectors were retained for fit prediction.

• 85% reduction in apparel returns caused by sizing mismatch
• 20× increase in customer engagement in smart fitting kiosks
• 22% uplift in eCommerce conversion rates
• 97% accuracy in clothing size recommendation

Technology

• Intel RealSense L515
• Open3D
• PyTorch
• Unity
• Core Language: Python

Dynamisch engineered a human digital twin platform capable of generating a high-fidelity 3D body model in under 10 seconds using LiDAR-based scanning. The system extracts detailed anthropometric measurements and applies machine learning models to predict brand-specific clothing sizes with high precision, enabling virtual try-on experiences across retail kiosks, scanning booths, and eCommerce platforms.