Learning a Shared Shape Space for Multimodal Garment Design
Tuanfeng Y. Wang, Duygu Ceylan, Jovan Popovic, Niloy J. Mitra

TL;DR
This paper introduces a data-driven system for garment design that allows users to sketch desired fold patterns and directly estimate corresponding garment and body shape parameters in real-time, streamlining the design process.
Contribution
It proposes a novel shared shape space enabling multimodal input specification without real-time garment simulation, improving efficiency and user control in digital garment design.
Findings
System achieves interactive rates for parameter estimation
Qualitative user study confirms usability and effectiveness
Quantitative tests show accurate garment and shape synthesis
Abstract
Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realistic dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped…
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