From Air to Wear: Personalized 3D Digital Fashion with AR/VR Immersive 3D Sketching
This work addresses the need for democratized fashion design on AR/VR platforms, though it is incremental as it builds on existing methods like diffusion models.
The paper tackles the problem of inaccessible 3D garment design tools for everyday users by introducing a 3D sketch-driven framework that allows creation of high-quality digital clothing through simple AR/VR sketches, with experiments showing it significantly outperforms baselines in fidelity and usability.
In the era of immersive consumer electronics, such as AR/VR headsets and smart devices, people increasingly seek ways to express their identity through virtual fashion. However, existing 3D garment design tools remain inaccessible to everyday users due to steep technical barriers and limited data. In this work, we introduce a 3D sketch-driven 3D garment generation framework that empowers ordinary users - even those without design experience - to create high-quality digital clothing through simple 3D sketches in AR/VR environments. By combining a conditional diffusion model, a sketch encoder trained in a shared latent space, and an adaptive curriculum learning strategy, our system interprets imprecise, free-hand input and produces realistic, personalized garments. To address the scarcity of training data, we also introduce KO3DClothes, a new dataset of paired 3D garments and user-created sketches. Extensive experiments and user studies confirm that our method significantly outperforms existing baselines in both fidelity and usability, demonstrating its promise for democratized fashion design on next-generation consumer platforms.