Leather Shoes Try-On Simulation Dataset
#image generation #deep learning #computer vision #virtual try-on #personalized recommendation #user experience enhancement
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-18
AI Analysis & Value Prop
The current retail e-commerce industry faces the challenge of users being unable to experience products realistically when shopping online, especially shoe products. Existing solutions provide user reviews and ratings but do not meet the users' needs for a true try-on experience. This dataset aims to offer high-quality leather shoe try-on simulation images through image synthesis technology to help users better choose the right shoe styles for themselves. Data collection mainly employs 3D modeling and image synthesis technology, generating images under various conditions with professional equipment. Quality control involves multiple rounds of labeling and expert reviews to ensure the authenticity and usability of the images. The data is stored in JPG format and organized by user characteristics and shoe style.
Dataset Insights
Sample Examples
35974619**.png|1280*1694|1.73 MB
a43af768**.png|1580*1098|856.32 KB
3f947b7a**.png|1280*812|934.70 KB
3eb313b8**.png|602*1280|875.94 KB
d4dbca0e**.png|961*1280|923.95 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| shoe_style | string | Description of the shoe style, such as casual shoes, dress shoes, etc. |
| shoe_color | string | Color of the shoe, such as black, brown, white, etc. |
| angled_orientation | string | The presentation angle of the shoes in the image, such as front view, side view, top view, etc. |
| lighting_conditions | string | Lighting conditions during the image capturing, referring to natural light, indoor lighting, etc. |
| background_scene | string | Description of the background scene in the image, such as plain background, indoor scene, etc. |
| textured_detail | string | Description of the texture or detailed features on the shoe surface. |
| pattern_description | string | Description of patterns on the shoe surface, such as checkered, striped, etc. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- How does this dataset help improve the user experience in retail e-commerce?
- The shoe try-on simulation dataset provides high-quality try-on images, allowing consumers to better preview product effects, thus enhancing purchase confidence.
- In which retail scenarios can this dataset be applied?
- This dataset can be applied in online shoe stores, virtual try-on applications, and augmented reality retail experiences.
- Does the shoe try-on simulation dataset support augmented reality technology?
- Yes, the dataset can support augmented reality technology, providing an interactive and immersive try-on experience.
- How can this dataset be used to improve customer purchasing decisions?
- By using the try-on images generated from this dataset, customers can perceive the appearance and fit of the shoes before purchasing, leading to more informed buying decisions.
- What image analysis techniques does this dataset support?
- The shoe try-on simulation dataset supports image recognition, object detection, and computer vision technologies, aiding retailers in developing intelligent applications.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Leather Shoes Try-On Simulation Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/6c8ee1773415226a2c13a3910ad70acf?dataset_scene_id=9},
urldate={2025-09-15},
keywords={leather shoe try-on dataset, image synthesis, retail e-commerce, virtual try-on, deep learning},
version={1.0}
}Using this in research? Please cite us.
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