Parametric Garment Space for 3D Mesh and Flat Pattern Generation

The patent replaces physics-based mechanical draping simulations with a machine learning model that directly maps 2D garment images to 3D mesh representations and 2D sewing patterns. This substitution eliminates the need for manual intervention in the flattening process while maintaining accurate garment representations, as the neural network learns the complex transformation relationships from training data without requiring explicit physics calculations or manual pattern making.

The system enables automatic self-service by allowing the machine learning model to autonomously perform the complete garment representation pipeline. Given only 2D input images, the model automatically generates both 3D mesh representations and flattened 2D sewing patterns without human intervention. The system serves itself by learning from training data and applying the learned transformations to new garments, eliminating the need for manual pattern making or physics-based simulation setup.

The patent applies preliminary action by pre-training the machine learning model on a large dataset of paired 2D garment images, 3D meshes, and 2D sewing patterns. This pre-training phase allows the model to learn robust feature representations and transformation relationships before deployment. When processing new garments with minimal input images, the pre-learned knowledge enables accurate 3D mesh and pattern generation even from limited input, as the model has already internalized garment structure relationships during pre-training.

The system handles minimal input images by changing the parameter representation through the neural network's latent space. The model transforms the limited input image parameters into a comprehensive set of parameters describing 3D mesh geometry and 2D pattern features. By learning efficient parameter mappings during training, the system can reconstruct complete garment representations from minimal input, effectively changing the parameter dimensionality and information density to maintain accuracy despite reduced input.

The patent substitutes complex physics-based mechanical simulations with a trained machine learning model. Instead of solving differential equations and performing iterative physics calculations to simulate fabric draping and wrinkles, the neural network directly predicts 3D mesh representations and 2D patterns from 2D input images. This replacement maintains realistic garment appearance by learning from training data while dramatically reducing computational complexity and processing time during inference.

The system uses copying by training the machine learning model on extensive datasets of realistic garment images, 3D meshes, and sewing patterns. The model learns to copy the complex relationships between 2D appearances, 3D structures, and flat patterns that exist in real garments. During deployment, it copies these learned relationships to generate accurate representations without requiring actual physics simulations, thereby maintaining realism while reducing computational burden.

The patent replaces manual pattern making mechanics with an automated machine learning system. The neural network directly generates precise 2D sewing patterns from 2D garment images and 3D mesh representations, eliminating manual measurement, drafting, and pattern making steps. The model learns accurate pattern geometry and seam line relationships from training data, maintaining manufacturing precision while reducing pattern generation time from hours or days of manual work to seconds or minutes of automated processing.

The system achieves self-service in pattern generation by allowing the machine learning model to autonomously produce sewing patterns without human intervention. Given 2D input images, the model automatically generates complete sets of 2D sewing patterns including piece layouts, seam lines, and cutting instructions. This self-service capability maintains precision through learned geometric relationships while eliminating the time and labor requirements of manual pattern making, enabling rapid customization and scaling.