Appearance generation for colored spun yarn fabric based on conditional image‐to‐image translation

The repeated trial-weaving and proofing process are traditionally conducted to exhibit the ever-changing appearance of the colored spun yarn fabric knitted by different colored spun yarns. In this paper, a novel method of appearance generation for colored spun yarn fabric was proposed based on conditional image-to-image translation. The generated image is required to have the same color and the fabric style simultaneously as the color card and the assigned style. The common pix2pix model was modified by adding the category label to the image channel to involve the style constraint. To improve the generation performance, the U-Net architecture was replaced by the residual block architecture. Four commonly used style fabrics and different color cards were adopted to build the image pairs for experiments. The visual inspection and category similarity were used as the evaluation metrics. Experimental results reveal that the proposed method can transfer different color cards to the designated style fabrics based on the constraints, being effective and superior for image generation of colored spun yarns. The proposed scheme can provide references for the designer by presenting the image perception, saving labor, and material resources in the proofing process.