AI Tool Sketch2Fit Turns Simple Sketches into 3D Fashion Prototypes
A new research tool developed by computer scientists at the University of Illinois Urbana-Champaign and Adobe Research is aiming to transform digital fashion design. Called Sketch2Fit, the AI-powered system converts 2D clothing sketches into fully draped 3D garments personalized to different body shapes.
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The tool was introduced in a preprint paper published on arXiv in May 2025. The lead authors include Yingya Ren, Hsiao-Yu Fish Tung, and David Forsyth—experts in computer vision and graphics modeling. Their goal: make digital garment prototyping as intuitive as sketching with a pencil.
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With Sketch2Fit, users don’t need any fashion background or 3D modeling skills. The system interprets freehand sketches, estimates garment patterns, simulates realistic cloth draping, and adapts the result to specific body measurements.
It works in three stages: sketch-to-pattern conversion, 3D cloth simulation, and body-fit refinement. The process is powered by deep learning and physics-based modeling, trained on a dataset of more than 10,000 garments.
Potential use cases span a broad range of industries. Independent fashion designers could create digital mockups for clients or production without specialized tools. Game developers and metaverse creators might rapidly generate avatar outfits for in-game characters or virtual events. Online retailers could offer customers previews of how garments might fit their digital body types, enhancing virtual try-ons and reducing returns. Even hobbyists and students may find a low-barrier way to explore clothing design.
Sketch2Fit is still a research prototype, but its creators highlight its potential for digital fashion designers, game developers, virtual stylists, and AR/VR platforms. By simplifying the pipeline, the tool could open up new possibilities for creative expression and personalization in digital wardrobes.
Read the full research paper on arXiv: Sketch2Fit: Personalized 3D Garment Modeling from 2D Sketches .
