The rapid construction method of human body model for virtual try-on on mobile terminal based on MDD-Net

Abstract

Traditional anthropometric evaluation needs professional measuring tools and operations, and it is time-consuming, expensive, and not suitable for virtual try-on. As the mobile internet develops, the issue of human body reconstruction toward virtual try-on needs to be tackled. This paper proposes a rapid human body reconstruction method for virtual try-on based on multidimensional dense net (MDD-Net) on mobile terminal. MDD-Net takes fusion features as input and predicts 3D human body model. The acquisition of fusion features and the display of 3D human body are implemented on mobile toward virtual try-on. In the learning fuzzy anthropometric feature module, the example-guided fuzzy anthropometric feature matrix is acquired and default coding elements are interpolated. In the learning multi-perspective silhouette feature module, the fine human body shape features are learned based on DenseNet201. A related fusion feature data set is generated for the training and testing of MDD-Net. Compared to shape pose estimation models, the shape representation spaces of HMR and SMPLify are only 20.34% and 7.59% of our method, and their prediction accuracies are approximately 50% of our method. Compared to accurate shape estimation models, our method is more robust against the pose and perspective noise. The prediction accuracies of our method are improved by 13.34%, 55.77%, 34.6%, and 43.4%, 37.2% 9.0% on four test sets. Extensive experiments have demonstrated the superiority of our method for human body shape estimation toward virtual try-on.