Batik Clothes Auto-Fashion using Conditional Generative Adversarial Network and U-Net

For Indonesian people, batik is one of the cultural heritages they are trying to preserve. Batik represents many regions in Indonesia, and each region has many motifs and also its usage. In this research, we propose a new method to apply batik patterns into a piece of clothing word by a person in a photo. Our proposed method consists of segmenting pieces of clothing from the images and blend them with our generated batik. We propose a CGAN with U-Net architecture to generate semantic segmentation on the image's clothes. This research also offers a batik motif generation system on the image's clothes system using CGAN with U-Net architecture. Experimental results show the advantages of our Batik Generative Method. Our experiments show that our best segmentation network can achieve 80.20% in F1 Measure and 70.32% in Jaccard Index. Our texture synthesis can achieve 99.992% in cosine similarity and 5.35 in Euclidean distance metric.