ImageInception/PRNet_3D

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:

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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