3D Shape-Adapted Garment Generation with Sketches

Abstract

Garment generation or reconstruction is becoming extremely demanding for many digital applications, and the traditional process is time-consuming. In recent years, garment reconstruction from sketch leveraging deep learning and principal component analysis (PCA) has made great progress. In this paper, we present a data-driven approach wherein 3D garments are directly generated from sketches combining given body shape parameters. Our framework is an encoder-decoder architecture. In our network, sketch features extracted by DenseNet and body shape parameters were encoded to latent code respectively. Then, the new latent code obtained by adding two latent codes of the sketch and human body shape is decoded by a fully convolutional mesh decoder. Our network enables the body shape adapted detailed 3D garment generation by leveraging garment sketch and body shape parameters. With the fully convolutional mesh decoder, the network can show the effect of body shape and sketch on the generated garment. Experimental results show that the fully convolutional mesh decoder we used to reconstruct the garment performs higher accuracy and maintains lots of detail compared with the PCA-based method.