arXiv:2511.18765v3 Announce Type: replace-cross Abstract: Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited. To acquire more realistic textures, generative methods are often used to extract Physically-based Rendering (PBR) textures and materials from large collections of wild images and project them back onto garment meshes. However, most image-conditioned texture generation approaches require strict topological consistency between the input image and the input 3D mesh, or rely on accurate mesh deformation to ma
Advances in generative AI, particularly in image synthesis and 3D modeling, are making sophisticated texture generation more feasible and necessary for virtual environments.
This development addresses a critical bottleneck in digital fashion and virtual world creation, enabling more realistic and diverse garment textures without complex manual intervention.
The ability to generate non-isometric, image-based garment textures reduces the need for strict mesh regularity, broadening the applicability of generative methods to existing 3D assets.
- · Digital fashion designers
- · Virtual world developers
- · E-commerce platforms
- · 3D content creators
- · Manual texture artists (for repetitive tasks)
- · Companies with limited 3D asset diversity
Automated texture generation accelerates the creation of diverse and realistic 3D garment assets for digital platforms.
Increased realism in virtual clothing could enhance user engagement and adoption of digital fashion and metaverse applications.
This technology might lower the barrier to entry for digital fashion creation, fostering a more personalized and decentralized virtual economy for clothing.
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Read at arXiv cs.AI