Slipper Family Try-On Image Dataset

The current retail e-commerce industry is facing the issue of insufficient online shopping experience for consumers, especially with footwear products where users cannot intuitively feel the actual effect and suitability. Existing solutions mostly rely on static images, lacking interactivity and failing to meet the growing personalized demands of consumers. This dataset aims to provide a rich collection of slipper family try-on images to help e-commerce platforms implement virtual try-on features, thereby enhancing the user shopping experience. The dataset structure includes fields such as image ID, image link, user ID, and try-on time. Data collection is conducted in collaboration with multiple e-commerce platforms using real user try-on data. Quality control involves multi-round annotations and expert review processes to ensure data accuracy and consistency. Data is stored in PNG and JPG formats and organized into structured files to facilitate subsequent deep learning and image synthesis tasks.