Can I read ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design on EtoBox?

ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design by Xujie Zhang; Yu Sha; Michael C. Kampffmeyer; Zhenyu Xie; Zequn Jie; Chengwen Huang; Jianqing Peng; Xiaodan Liang is a scholarly article available to read on EtoBox.

What is ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design about?

Cross-modal fashion image synthesis has emerged as one of the most promising directions in the generation domain due to the vast untapped potential of incorporating multiple modalities and the wide range of fashion image applications. To facilitate accurate generation, cross-modal synthesis methods typically rely on Contrastive Language-Image Pre-training (CLIP) to align textual and garment information. In this work, we argue that simply aligning texture and garment information is not sufficient to capture the semantics of the visual information and therefore propose MaskCLIP.

Author

Xujie Zhang; Yu Sha; Michael C. Kampffmeyer; Zhenyu Xie; Zequn Jie; Chengwen Huang; Jianqing Peng; Xiaodan Liang

Publisher

ACM

Published

2022

Language

EN