SVTON: Simplified Virtual Try-On

2D based Virtual Try-On (VTON) has been trending towards using human parsing to improve the quality of the try-on image. However, it remains a challenging problem for most existing VTON models to generate realistic images for situations with unpaired candidate-clothing images and body-part occlusions. We have developed a Simplified Virtual Try-On (SVTON) model to rectify the above problem. The SVTON uses refined input data to produce accurate labels and has fewer trainable parameters than existing methods. Also, it is designed with a simplified network architecture for segmentation and an efficient Affine Transform for warping to target clothing. Experiments on benchmark datasets show that the proposed model performs better than the state-of-the-art VTON models for unpaired and occlusion cases, while maintaining the similar overall performance level for normal cases.