| ### Model Card: **PRNet 3D Face Reconstruction** |
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| **Model Name**: `PRNet_3D` |
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| #### Model Description: |
| PRNet is a deep learning model for **3D face reconstruction** from a single 2D image. This model regresses a 3D position map and reconstructs dense facial landmarks from 2D inputs. The fine-tuned version of PRNet has been optimized to handle facial images more robustly in the provided domain. |
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| - **Architecture**: Position Map Regression Network (PRNet) |
| - **Base Model**: Pre-trained PRNet (before fine-tuning) |
| - **Training Data**: Custom dataset of 2D facial images and corresponding 3D meshes. |
| - **Purpose**: The model is used for forensic investigations, facial recognition, and 3D modeling from 2D images. |
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| #### Model Details: |
| - **Training**: Fine-tuned using Google Colab with TensorFlow 1.x and the PRNet architecture. The model was trained on a specific dataset of 2D face images and optimized for 3D face reconstruction. |
| - **Outputs**: The model outputs a `.obj` file that contains the 3D mesh representation of the input 2D image. |
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| #### Usage: |
| This model is intended for **3D face reconstruction** tasks. It takes a 2D facial image and outputs a 3D `.obj` file of the reconstructed face. |
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| ##### Example: |
| ```python |
| from your_project_module import PRN |
| import numpy as np |
| from skimage.io import imread |
| |
| prn = PRN(is_dlib=False) # Initialize the model without dlib |
| image = imread('path_to_image.jpg') |
| image = resize(image, (256, 256)) # Resize image to 256x256 |
| |
| # Process and generate 3D vertices |
| pos = prn.net_forward(image / 255.0) |
| vertices = prn.get_vertices(pos) |
| ``` |
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| #### Intended Use: |
| - **Forensic Investigations**: Reconstruction of faces from low-quality images for law enforcement or identification purposes. |
| - **3D Modeling**: Generates 3D models from 2D images for entertainment, games, or medical applications. |
| - **Facial Recognition**: Can be used for generating 3D facial profiles for use in recognition systems. |
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| #### Limitations and Risks: |
| - **Accuracy in Reconstruction**: The accuracy of the 3D reconstruction depends heavily on the quality and resolution of the input 2D image. |
| - **Bias and Dataset Limitations**: Since the model is fine-tuned on a specific dataset, there may be biases or limitations when applied to other types of facial structures or ethnicities. |
| - **Sensitivity to Image Quality**: Low-quality images may produce less accurate 3D models or fail entirely to reconstruct. |
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| #### How to Cite: |
| If you use this model, please cite the original PRNet authors and mention the fine-tuned adjustments: |
| ``` |
| @misc{prnet_3d_finetuned, |
| title={PRNet 3D Face Reconstruction Finetuned Model}, |
| author={Mostafa Aly}, |
| year={2024}, |
| howpublished={\url{https://huggingface.co/your-hf-username/PRNet_3D}}, |
| } |
| ``` |
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| ### How to Use These Model Cards: |
| 1. **Create a Hugging Face Space** for each of your models if you haven't already. |
| 2. **Upload the model files** and include these model cards as markdown files (`README.md`) in the repository. |
| 3. **Customize the links** and placeholders like `"your-hf-username"` and `"path_to_image.jpg"` to your own project details. |
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| Let me know if you need further assistance with publishing or tweaking the cards! |
