OFP-VTON:基于感受野模块的原有特征保持虚拟试衣网络OA

Computer vision-based virtual try-on(VITON)technology refers to warping and composing the try-on clothing according to the model image features into the model image to replace the original clothing parts.Current VITON methods have two main challenges:insufficient preservation of original features such as the head,bottom,and background of the model image;poor matching of the warped try-on clothing to the model image.To solve these two problems,an original feature preserving virtual try-on network(OFP-VTON)is proposed,which consists of semantic segmentation map generation,try-on clothing warping,and try-on image synthesis.In the try-on clothing warping phase,the network learns the mapping of warping of the clothing worn in the model image to better constrain the try-on warping.In the try-on image synthesis phase,the original features of the model image are extracted and preserved,and a receptive field block(RFB)is introduced to preserve the features of try-on clothing as much as possible.Qualitative and quantitative experiments on the publicly available VITON dataset show that the proposed OFP-VTON better preserves the original features and that the warped try-on clothing matches the model images better than the baseline method.