| ### Model Card: **PRNet 3D Face Reconstruction** | |
| **Model Name**: `PRNet_3D` | |
| #### 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. | |
| - **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. | |
| #### 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. | |
| #### 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. | |
| ##### 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) | |
| ``` | |
| #### 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. | |
| #### 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. | |
| #### 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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