EASE: Parametric garment design with explicit and local ease control

2026-06-28

AI Summary

This work addresses the longstanding challenge in traditional garment design where ease—the intentional looseness of clothing relative to the body—cannot be explicitly controlled, edited, or transferred as an independent variable, often relying instead on geometric modeling or physical simulation with limited direct manipulability. The authors propose a novel representation that embeds garment meshes into a parametric human body model, explicitly encoding local ease through a spatially varying anisotropic triangle scaling field. This field serves as the core design variable and is integrated with user-defined pattern pieces and geometric-stitching constraints to optimize seam patterns. The method enables, for the first time, localized and explicit control over ease, supporting direct editing, intent-preserving transfer across body shapes, and pose-driven redistribution. Experiments demonstrate high fidelity in ease preservation, significant reduction of excessive stretching in target poses, and accurate transfer to novel body types. The system is publicly released.

Abstract

Garment fit and comfort depend critically on ease, the local allowance of excess material relative to the body. In existing design pipelines, ease is typically a byproduct of geometry or simulation rather than an independent design variable, making it difficult to specify, edit, transfer, or redistribute without re-running simulation or optimization. We propose a garment representation that embeds meshes directly on the surface of a parametric human body model and represents ease explicitly as spatially varying, anisotropic per-triangle scales. These scales act as primary design variables, decoupling the specification of material allowance from its physical deformation. Given a design specified by parametric and user-defined surface cuts together with local scale fields, we optimize sewing patterns that enforce the prescribed ease distribution while satisfying geometric and seam constraints. The representation enables three capabilities that are unavailable without explicit ease control: (1) direct specification and editing of local material allowance on the body surface; (2) intent-preserving transfer to new body shapes that reproduces the specified ease distribution without re-running simulation; and (3) intent-modifying pose adaptation that redistributes ease to relieve strain in high-stretch regions. We verify each of these experimentally: ease is closely retained after optimization, excessive strain is significantly mitigated for target poses, and the ease distribution is accurately transferred to target shapes. The approach is implemented as a virtual try-on framework, with physics-based cloth simulation used for final garment visualization. We will publicly release our framework and detailed documentation.

Problem

• garment design
• ease control
• parametric modeling
• cloth simulation
• fit and comfort

Innovation

• ease control
• parametric garment design
• anisotropic scaling
• virtual try-on
• pose adaptation