Sweatpants Motion Try-On Dataset

With the rapid growth of online retail e-commerce, the demand for personalized shopping experiences is increasing. However, most e-commerce platforms currently rely on static images for the try-on experience, lacking realism, which makes it difficult for users to accurately assess the fit of clothing, thus affecting purchase conversion rates. Existing virtual try-on solutions mostly use simple image processing techniques, lacking realistic simulation of dynamic movements. This dataset is constructed to provide synthetic images of various sweatpants under different movements, aimed at enhancing the online try-on experience. The dataset includes 5000 images generated using high-quality image synthesis techniques, covering various movements and models, effectively supporting the development of virtual try-on technologies. Data collection utilizes advanced image synthesis tools to simulate real try-on scenarios, ensuring diversity and authenticity of images. To ensure data quality, we implemented multiple rounds of annotation and consistency checks to ensure the accuracy of each sample. Additionally, all data is stored in JPEG format, organized by model and movement type for easy retrieval and use.