Q: What is the end-to-end workflow (file upload → task run → polling → result)?

The standard workflow is: Upload files using the File API to obtain file_id s Run the AR bag try-on task using the task API Poll the task status until it returns success or error Retrieve the result , including the output image URL and result file_id Polling is required to prevent task timeout and ensure successful completion.

Question 1

Do I need to provide professional 3D models to use this API?

Accepted Answer

No, you just need to upload a photo of the user and a reference image of the bag to enjoy the AR Bag virtual try-on experience.

Question 2

Does it support mobile web browsers (Web AR)?

Accepted Answer

Yes, our API fully supports mobile browsers, allowing users to enjoy a smooth virtual try-on experience without downloading an app.

Question 3

Is the try-on a 2D overlay or AI-based realistic fitting?

Accepted Answer

The AR Bag Try-On uses AI-based realistic fitting, not a simple 2D overlay. The system analyzes the user image to place the bag naturally on the body, adjusting scale, position, and perspective to achieve a more realistic try-on result.

Question 4

Does the API automatically detect body pose and shoulder position?

Accepted Answer

Yes. The API performs automatic body and pose detection, including key areas such as shoulders and upper body. This allows the bag to be positioned correctly for different wearing styles, such as shoulder bags or crossbody bags, without manual alignment.

Question 5

What type of user image is required (full body / half body)?

Accepted Answer

A half-body or full-body image is recommended. The upper body and shoulders should be clearly visible for best results. Images with good lighting, a clear pose, and minimal occlusion will produce more accurate try-on outcomes.

Question 6

What formats are supported for bag images (PNG, JPG, transparent background)?

Accepted Answer

Bag images typically support PNG and JPG formats. Using PNG with a transparent background is strongly recommended, as it helps the AI place the bag cleanly onto the user image without background artifacts.

Question 7

What is the end-to-end workflow (file upload → task run → polling → result)?

Accepted Answer

The standard workflow is:

Upload files using the File API to obtain file_ids
Run the AR bag try-on task using the task API
Poll the task status until it returns success or error
Retrieve the result, including the output image URL and result file_id
Polling is required to prevent task timeout and ensure successful completion.

Question 8

How long does a typical AR bag try-on task take?

Accepted Answer

A typical AR bag try-on task usually completes within a few seconds, depending on image size, server load, and pose complexity. The task runs asynchronously, so polling should be implemented to track completion.

AI Bag Virtual Try-On API

Preview outfits with bag immediately with the hyper-realistic AR tech. Our RESTful API integrates seamlessly with your website, app, or in-store mirrors.

Cutting-edge AI & AR Technology Integrated into a Single API Call

Enable Virtual Try-On with Just One Image

From Product Photos to Virtual Try-On

Hyper-Realistic AR Bag Rendering

Instant Virtual Try-on Visualization

AI-Generated Scenes

Powerful AI Capabilities, Seamlessly Integrated in Just a Few Steps

Advanced AI Auto-Scaling

Seamless Cross-Platform Integration

Lightweight & Low Latency

How Does the Bag Virtual Try-on API Work?

Versatile Image Input

Retrieve File API Response

Prepare a Reference Bag Image

Retrieve Result

Bag Virtual Try-On API FAQs