Fashion-model pose recommendation and generation using Machine Learning
This addresses a domain-specific need for fashion personnel, such as models and photographers, by providing pose recommendations and synthetic image generation, though it is incremental as it builds on existing methods like StyleGAN.
The paper tackles the problem of fashion-model pose uncertainty by developing a system that recommends similar images based on color histogram segmentation and generates synthetic images using StyleGAN to reduce photoshoot costs and privacy issues.
Fashion-model pose is an important attribute in the fashion industry. Creative directors, modeling production houses, and top photographers always look for professional models able to pose. without the skill to correctly pose, their chances of landing professional modeling employment are regrettably quite little. There are occasions when models and photographers are unsure of the best pose to strike while taking photographs. This research concentrates on suggesting the fashion personnel a series of similar images based on the input image. The image is segmented into different parts and similar images are suggested for the user. This was achieved by calculating the color histogram of the input image and applying the same for all the images in the dataset and comparing the histograms. Synthetic images have become popular to avoid privacy concerns and to overcome the high cost of photoshoots. Hence, this paper also extends the work of generating synthetic images from the recommendation engine using styleGAN to an extent.