YadiraF/PRNet
PRNet
PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial geometry and vertex colors from a single 2D input image to generate a textured mesh.
The project provides tools for digital face swapping, allowing the replacement of a target face with a new image and blending textures to match the original pose. It also includes a framework for face texture swapping and blending to fit specific 3D poses.
Additional capabilities cover facial analysis, including the detection and alignment of facial landmarks and the estimation of head pose and camera orientation matrices.
Features
- Single-Image 3D Reconstructions - Creates a complete 3D facial model from a single photograph using a regression-based deep learning model.
- Head Pose Estimation - Implements techniques for calculating the 3D orientation and position of a head relative to a camera.
- Face Swapping - Replaces a target face with a new image while blending textures to match the original pose.
- Face Swapping Frameworks - Provides a deep learning framework for identity replacement and texture blending in visual media.
- Facial Landmark Analysis - Determines the position and visibility of key facial landmarks for feature alignment.
- Facial Landmark Detection - Identifies and tracks key facial landmarks from a single input image to facilitate reconstruction.
- Automated Blending - Uses deep learning to resolve color and lighting discrepancies when blending textures onto 3D face meshes.
- Computer Vision Research - Joint 3D face reconstruction and dense alignment via regression.
- Face Alignment - Joint 3D reconstruction and dense alignment via position maps.
- Face Alignment And Reconstruction - Joint 3D face reconstruction and dense alignment via regression.
- Faces: - Listed in the “Faces:” section of the Curated List Of Awesome 3D Morphable Model Software And Data awesome list.
- Keypoint detection - Listed in the “Keypoint detection” section of the Ailia Models awesome list.
