To study the correlation between clothing garments and body shape, we collected a new dataset (Fashion Takes Shape), which includes images of female users with clothing category annotations. Despite the progress in body shape estimation from images, it turns out to be challenging to infer body shape from such diverse, real-world photos. Hence, we propose a novel and robust multi-photo approach to estimate body shapes of each user and build a conditional model of clothing categories given body-shape. We demonstrate that in real-world data, clothing categories and body-shapes are correlated and show that our multi-photo approach leads to a better predictive model for clothing categories compared to models based on single-view shape estimates or manually annotated body types. We see our method as the first step towards the large-scale understanding of clothing preferences from body shape.
IEEE Workshop on Applications of Computer Vision (WACV)
2019-01-07
2024-09-06