Hierarchical Cross-Attention Network for Virtual Try-On

TL;DR

This paper introduces HCANet, a hierarchical cross-attention network that improves virtual try-on by capturing detailed interactions between clothing and person, achieving state-of-the-art realism and accuracy.

Contribution

The paper proposes a novel Hierarchical Cross-Attention Network with a unique HCA block for enhanced detail and robustness in virtual try-on tasks.

Findings

HCANet outperforms existing methods in realism and accuracy.

The hierarchical approach effectively captures intricate person-clothing interactions.

Experimental results validate the superiority of HCANet in quantitative and subjective evaluations.

Abstract

In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of HCANet is the incorporation of a novel Hierarchical Cross-Attention (HCA) block into both stages, enabling the effective capture of long-range correlations between individual and clothing modalities. The HCA block enhances the depth and robustness of the network. By adopting a hierarchical approach, it facilitates a nuanced representation of the interaction between the person and clothing, capturing intricate details essential for an authentic virtual try-on experience. Our experiments establish the prowess of HCANet. The results showcase its performance across both quantitative metrics and subjective evaluations of visual realism. HCANet stands out as a state-of-the-art solution, demonstrating its capability to generate virtual try-on results that excel in accuracy and realism. This marks a significant step in advancing virtual try-on technologies.