Towards Vision-Language-Garment Models for Web Knowledge Garment Understanding and Generation

TL;DR

This paper introduces VLG, a multimodal vision-language-garment model that synthesizes garments from text and images, demonstrating promising zero-shot generalization to unseen styles and prompts in fashion design.

Contribution

The paper presents VLG, a novel model that extends multimodal foundation models to the specialized domain of garment synthesis and understanding.

Findings

VLG shows strong zero-shot transfer to unseen garment styles.

Preliminary results indicate effective reasoning in fashion domain.

VLG can generate garments from textual descriptions and images.

Abstract

Multimodal foundation models have demonstrated strong generalization, yet their ability to transfer knowledge to specialized domains such as garment generation remains underexplored. We introduce VLG, a vision-language-garment model that synthesizes garments from textual descriptions and visual imagery. Our experiments assess VLG's zero-shot generalization, investigating its ability to transfer web-scale reasoning to unseen garment styles and prompts. Preliminary results indicate promising transfer capabilities, highlighting the potential for multimodal foundation models to adapt effectively to specialized domains like fashion design.