Hierarchical Cross-Attention Network for Virtual Try-On
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915031545806848 |
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| author | Tang, Hao Ren, Bin Wu, Pingping Sebe, Nicu |
| author_facet | Tang, Hao Ren, Bin Wu, Pingping Sebe, Nicu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_15542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Cross-Attention Network for Virtual Try-On Tang, Hao Ren, Bin Wu, Pingping Sebe, Nicu Computer Vision and Pattern Recognition 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. |
| title | Hierarchical Cross-Attention Network for Virtual Try-On |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.15542 |