Neural Geometry Details for Real-Time Multi-Scale Cloth Simulation

Problem Statement

Current cloth simulation methods face a fundamental trade-off between performance and detail:

• Coarse-resolution simulations achieve real-time performance but lack fine geometric details like yarn-level wrinkles and fabric microstructure
• High-resolution simulations capturing yarn dynamics are computationally prohibitive for interactive applications
• Existing wrinkle synthesis techniques add visual detail but often lack physical accuracy and fail to capture complex mechanical behavior of fabric microstructure

Motivation

Physically accurate, real-time cloth simulation with fine detail is critical for:

• Virtual try-on systems and digital fashion design
• Video games and VR/AR environments
• Film production and visual effects
• Material science and textile engineering research

The ability to simulate yarn-level fabric behavior in real-time would enable more realistic virtual garments and improve the quality of interactive design tools.

Proposed Approach

A three-level geometric learning framework combining coarse physics simulation with learned yarn-level dynamics:

• Coarse Level: Leverage existing real-time physics simulators (XPBD, Position-Based Dynamics) for low-resolution cloth mesh
• Fine Level: Train neural networks to predict yarn-level displacement fields as functions of coarse mechanical features (strain tensors, curvature, stretch)
• Reconstruction Level: Apply parametric surface interpolation (Coons patches) to reconstruct high-resolution cloth by composing coarse geometry with learned fine displacements

Key Insight: Coarse simulation produces locally smooth 2D parametric surfaces that serve as a geometric canvas for fine details, enabling efficient learned detail synthesis while preserving physics.

Novelty

• Geometric reconstruction framework: Formulating fine detail prediction as learning displacement fields in parametric patch coordinates with Coons-based C0/C1 continuous reconstruction
• Physics-informed representation: Learning yarn dynamics conditioned on physically meaningful coarse features rather than purely data-driven synthesis
• Unified multi-scale approach: Tight integration between classical physics simulation and learned mechanics through principled geometric interface

Relationship to Geometric Modeling

This work fundamentally addresses geometric modeling at multiple scales by:

• Leveraging classical surface representations (parametric patches, ruled surfaces)
• Developing geometric interpolation schemes for multi-resolution reconstruction
• Learning mappings between geometric and mechanical features at different scales

The parametric surface framework provides the mathematical structure connecting coarse and fine geometry.

Project Milestones

Phase 1: Foundation & Literature Review

• Survey state-of-the-art cloth simulation methods (PBD, XPBD, FEM-based)
• Review neural cloth simulation and learning-based wrinkle synthesis
• Study parametric surface reconstruction techniques (Coons patches, subdivision surfaces, displacement mapping)
• Research yarn-level fabric mechanics and multi-scale textile simulation
• Implement baseline coarse cloth simulator using existing framework

Phase 2: Core Development

• Generate training dataset: High-resolution yarn-level cloth simulations (hanging cloth, draping scenarios)
• Design neural architecture for predicting yarn displacement fields from coarse features
• Implement and train initial model
• Develop Coons patch reconstruction module for geometric interpolation
• Perform preliminary qualitative evaluation against ground truth

Phase 3: Integration & Evaluation

• Complete end-to-end pipeline integrating coarse simulation, learned prediction, and geometric reconstruction
• Achieve real-time or near-real-time performance
• Develop interactive demo
• Quantitative evaluation:
  • Geometric error metrics (vertex position error, normal deviation)
  • Physical plausibility (strain energy comparison)
  • Performance benchmarks (FPS, inference time)

Stretch Goals

• Generalization across different draping scenarios and fabric types
• Handle challenging cases (sharp folds, self-contact with fine details)