---
tags:
- image generation
- image recognition
- deep learning
- online try-on
- virtual fitting
- personalized recommendation
license: cc-by-nc-sa-4.0
task_categories:
- image-to-image
language:
- en
pretty_name: High Heel AI Try-On Image Collection
size_categories:
- 1B<n<10B
---

# High Heel AI Try-On Image Collection

The current retail e-commerce industry faces the issue of customers not being able to experience products authentically while shopping online, especially for footwear like high heels, where consumers often abandon purchases due to the inability to try them on. Existing solutions, such as static image displays, fail to meet consumers&#039; demand for a realistic sense of the product. This dataset aims to address the technical issue of the lack of online try-on experiences for consumers by providing synthetic high heel try-on images. The structure of the dataset includes a unique ID for each image, image links, shoe type, and model ID. Data collection is achieved using advanced image synthesis technology, combining the postures of real models with high heel images to generate high-quality try-on effects. To ensure data quality, multiple rounds of annotation and consistency checks are performed to ensure the authenticity of each synthetic image. The data storage format is JPG, organized by shoe type and model classification.

## Technical Specifications

| Field | Type | Description |
| :---  | :---  | :--- |
| file_name | string | File name |
| quality | string | Resolution |
| shoe_color | string | Identify the color of the high heels in the image. |
| shoe_type | string | Identify the type of high heels in the image, such as pointed, round, etc. |
| heel_height | float | Identify the heel height of the high heels in the image in centimeters. |
| shoe_material | string | Identify the material of the high heels in the image, such as leather, fabric, etc. |
| foot_position | string | Identify the posture or position of the foot in the image. |
| background_type | string | Identify the type of background in the image, such as indoor, outdoor, etc. |
| lighting_condition | string | Identify the lighting condition of the image, such as bright, dim. |

## Compliance Statement

<table>
  <tr>
    <td>Authorization Type</td>
    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
  </tr>
  <tr>
    <td>Commercial Use</td>
    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
  </tr>
  <tr>
    <td>Privacy and Anonymization</td>
    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
  </tr>
  <tr>
    <td>Compliance System</td>
    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
  </tr>
</table>

## Source & Contact

If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/82db8d11208cd0d8f826c1f9e866b44a?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com