Fashion-PaddlePaddle

Capture user photo via mobile app.

Use Fashion-PaddlePaddle to parse the user's current body shape and pose.

Select a product image from the catalog.

Apply GAN-based warping to overlay the product on the user.

Render the final composite for user preview.

Upload batch product photos to the server.

Run the Attribute Recognition model to extract features like 'V-neck', 'Floral', and 'Midi'.

Generate JSON metadata for each image.

Push metadata directly to the PIM (Product Information Management) system.

Scrape public social media fashion feeds.

Filter images using the Fashion Landmark Detection model to ensure human presence.

Run parsing to identify popular colors and garment types.

Aggregate data into a trend dashboard for design teams.

Process live video stream from smart mirror camera.

Execute low-latency pose estimation via Paddle Lite.

Render digital clothing layers over the physical user reflection.

Allow gesture-based style switching.

Input two photos of the customer (front and side).

Run the segmentation model to extract body contours.

Calculate volumetric estimates based on keypoints.

Match estimates against the brand's sizing chart.

User uploads a 'street snap' image.

Model detects and crops fashion items within the image.

Vectorize the cropped items using a feature extraction model.

Perform a nearest-neighbor search against the store's inventory.

Input a 2D fabric pattern and a 3D model pose.

Utilize the GAN synthesis module to simulate fabric drape.

Output a high-fidelity visualization for design approval.

Adjust colors and textures dynamically.