Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources

Abstract

To study the correlation between clothing garments and body shape, we collected a new dataset (Fashion Takes Shape), which includes images of users with clothing category annotations. We employ our 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.

References in corpus (11)

• Fashion-Gen: The Generative Fashion Dataset and Challenge
• Towards Accurate Markerless Human Shape and Pose Estimation over Time
• Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
• Unite the People: Closing the Loop Between 3D and 2D Human Representations
• Automatic Spatially-aware Fashion Concept Discovery
• Video Based Reconstruction of 3D People Models
• VITON: An Image-based Virtual Try-on Network
• Fashion Forward: Forecasting Visual Style in Fashion
• Detailed, accurate, human shape estimation from clothed 3D scan sequences
• Learning to Estimate 3D Human Pose and Shape from a Single Color Image
• Creating Capsule Wardrobes from Fashion Images