Hierarchical Cross-Attention Network for Virtual Try-On

• URL: http://arxiv.org/abs/2411.15542v1
• Date: Sat, 23 Nov 2024 12:39:58 GMT
• Title: Hierarchical Cross-Attention Network for Virtual Try-On
• Authors: Hao Tang, Bin Ren, Pingping Wu, Nicu Sebe,
• Abstract summary: 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.
• Score: 59.50297858307268
• License:
• 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.

Related papers

• Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models [4.038493506169702]
  This study emphasizes the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios. Various existing approaches are explored, highlighting the limitations and unresolved aspects. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on.
  arXiv  Detail & Related papers  (2024-03-12T07:15:29Z)
• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval [59.15034487974549]
  We propose a novel COllaborative Two-Stream vision-language pretraining model termed COTS for image-text retrieval. Our COTS achieves the highest performance among all two-stream methods and comparable performance with 10,800X faster in inference. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-ofthe-art on the widely-used MSR-VTT dataset.
  arXiv  Detail & Related papers  (2022-04-15T12:34:47Z)
• Cloth Interactive Transformer for Virtual Try-On [106.21605249649957]
  We propose a novel two-stage cloth interactive transformer (CIT) method for the virtual try-on task. In the first stage, we design a CIT matching block, aiming to precisely capture the long-range correlations between the cloth-agnostic person information and the in-shop cloth information. In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask.
  arXiv  Detail & Related papers  (2021-04-12T14:45:32Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.