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A Comparative Study of Garment Draping Techniques

Prerana Achar, Mayank Patel, Anushka Mulik, Neha Katre, Stevina Dias, Chirag Raman

TL;DR

This paper addresses the challenge of selecting realistic garment draping techniques for 3D fashion design, virtual try-ons, and animations. It provides a comprehensive review contrasting physics-based, differentiable, and machine learning–based approaches across representations, material behavior, time integration, and collision handling. The work offers a structured taxonomy, synthesis of data requirements and computation trade-offs, and practical guidelines for choosing methods in multilayer garment draping. The findings help researchers and developers balance realism, efficiency, and data needs in applications spanning digital fashion, AR/VR, and computer graphics.

Abstract

We present a comparison review that evaluates popular techniques for garment draping for 3D fashion design, virtual try-ons, and animations. A comparative study is performed between various methods for garment draping of clothing over the human body. These include numerous models, such as physics and machine learning based techniques, collision handling, and more. Performance evaluations and trade-offs are discussed to ensure informed decision-making when choosing the most appropriate approach. These methods aim to accurately represent deformations and fine wrinkles of digital garments, considering the factors of data requirements, and efficiency, to produce realistic results. The research can be insightful to researchers, designers, and developers in visualizing dynamic multi-layered 3D clothing.

A Comparative Study of Garment Draping Techniques

TL;DR

This paper addresses the challenge of selecting realistic garment draping techniques for 3D fashion design, virtual try-ons, and animations. It provides a comprehensive review contrasting physics-based, differentiable, and machine learning–based approaches across representations, material behavior, time integration, and collision handling. The work offers a structured taxonomy, synthesis of data requirements and computation trade-offs, and practical guidelines for choosing methods in multilayer garment draping. The findings help researchers and developers balance realism, efficiency, and data needs in applications spanning digital fashion, AR/VR, and computer graphics.

Abstract

We present a comparison review that evaluates popular techniques for garment draping for 3D fashion design, virtual try-ons, and animations. A comparative study is performed between various methods for garment draping of clothing over the human body. These include numerous models, such as physics and machine learning based techniques, collision handling, and more. Performance evaluations and trade-offs are discussed to ensure informed decision-making when choosing the most appropriate approach. These methods aim to accurately represent deformations and fine wrinkles of digital garments, considering the factors of data requirements, and efficiency, to produce realistic results. The research can be insightful to researchers, designers, and developers in visualizing dynamic multi-layered 3D clothing.
Paper Structure (18 sections, 5 figures, 1 table)

This paper contains 18 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Representations
  4. 3D Meshes
  5. 3D Point Clouds
  6. Neural Fields
  7. Physics Based Methods
  8. Material Behaviour
  9. Time Integration and Acceleration
  10. Differentiable Simulation
  11. Machine Learning Based Methods
  12. Learned Deformation Models
  13. Implicit Representation
  14. Graph Neural Networks (GNN)
  15. Training Methods
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Graphical representation of methods
  • Figure 2: Category vs. Year Published
  • Figure 3: PRISMA flow diagram for this comparative study
  • Figure 4: Representations
  • Figure 5: Distribution of 3D Representation techniques