基于并行卷积核的Attention U-Net虚拟试衣方法研究

Research on Attention U-Net Virtual Try-On Method based on Parallel Convolution Kernel

针对虚拟试衣中特征提取不足、人物肢体被衣服遮挡的问题,在基于图像特征保留的虚拟试衣方法基础上,提出基于并行卷积核的Attention U-Net虚拟试衣方法。该方法采用并行卷积核代替原有的3×3卷积核来提取特征,并在U-Net网络中融入注意力机制形成新的Attention U-Net图像合成器,通过不断调整网络学习参数,将模型放在数据集VITON Dataset上进行虚拟试衣实验。实验结果表明,与原方法相比,该方法能提取出更多的细节纹理,在结构相似性上提升了15.6%,虚拟试衣效果更好。

Virtual try-on has problem of insufficient feature extraction in and people's limbs being covered by clothes.On the basis of the virtual try-on method with image feature retention,this paper proposes an Attention U-Net virtual try-on method based on parallel convolution kernel.In this method,parallel convolution kernel is used to replace the original 3×3 convolution kernel to extract features,and the attention mechanism is integrated into the u-net network to form a new Attention U-Net image synthesizer.By constantly adjusting the network learning parameters,the model is placed on the data set VITON(Virtual Try-On Network)Dataset for virtual fitting experiment.Experimental results show that compared with the original method,the proposed method can extract more detailed textures,improve the structural similarity by 15.6%,and the virtual fitting effect is better.