Garment3DGen: 3D Garment Stylization and Texture Generation
Nikolaos Sarafianos, Tuur Stuyck, Xiaoyu Xiang, Yilei Li, Jovan, Popovic, Rakesh Ranjan

TL;DR
Garment3DGen is a novel method that synthesizes 3D textured garments from a single image, enabling realistic draping and simulation without requiring artistic input, by combining image-to-3D diffusion and mesh deformation techniques.
Contribution
It introduces a new pipeline that uses 3D diffusion outputs as pseudo ground-truth for mesh deformation, producing high-quality, simulation-ready 3D garments from minimal input.
Findings
Effective generation of textured 3D garments from single images.
High fidelity textures that match input guidance.
Applications demonstrated in VR and sketch-to-garment workflows.
Abstract
We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such as those generated by text prompts. The generated assets can be directly draped and simulated on human bodies. We leverage the recent progress of image-to-3D diffusion methods to generate 3D garment geometries. However, since these geometries cannot be utilized directly for downstream tasks, we propose to use them as pseudo ground-truth and set up a mesh deformation optimization procedure that deforms a base template mesh to match the generated 3D target. Carefully designed losses allow the base mesh to freely deform towards the desired target, yet preserve mesh quality and topology such that they can be simulated. Finally, we generate high-fidelity…
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Taxonomy
Topics3D Shape Modeling and Analysis · Industrial Vision Systems and Defect Detection · Additive Manufacturing and 3D Printing Technologies
MethodsSparse Evolutionary Training · Diffusion · Balanced Selection
