Parametric 3D clothing generation: From sketch to dynamic fit

Traditional clothing design methods face challenges in efficiency and adaptability, often requiring manual adjustments and lacking in physical plausibility. This study introduces a data-driven, parametric 3D clothing generation framework that leverages garment sketches and dynamic 3D human parameters to create digital clothing. Our work is situated within the digital apparel design pipeline, bridging the gap between conceptual sketching and physically plausible virtual garments. Utilizing a dense convolutional network and a dual-branch encoder, our method efficiently recognizes design intent and dynamically adapts to body characteristics. The “fit” is defined as the geometric and physical compatibility between the garment mesh and the human body surface, avoiding interpenetration while maintaining design features. Experimental results demonstrate high generation quality (chamfer distance of 0.87 mm, detail matching rate of 91%) and efficiency (45 ms for a 100,000-vertex mesh). To quantitatively assess the improvement in design efficiency, we conducted a comparative study with 40 designers using both traditional CAD tools and our proposed framework. The total design time per garment was reduced from 390 min using traditional CAD to 55 min using our method, representing an 85.9% time saving. Key stages such as sketch drawing (96.7% reduction), pattern making (100% reduction), and 3D modeling (100% reduction) were significantly accelerated. Furthermore, the number of designs produced within a 3-h window increased from 0.8 to 3.2, a 300% improvement in design output. The efficiency gap between novice and expert designers was reduced by 38.1%, indicating that the method lowers the technical barrier for entry. These quantitative results confirm that the proposed framework substantially enhances design productivity and accessibility. The framework effectively accommodates unusual body types, exceeding an 85% adaptation rate. This approach offers a viable solution for intelligent clothing design and virtual try-on, contributing to the digital transformation of the fashion industry.