3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from a single two-dimensional input image. The project provides utilities for estimating facial pose and depth maps to determine the orientation and position of a face. It includes a geometry exporter to save reconstructed facial shapes into standard file formats for use in external 3D modeling software. The software further covers automated face cropping using landmark-based and landmark-free identification methods, as well as the extraction of specialized mathematical represent
This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p
This project is a computer vision tool designed to calculate the pitch, yaw, and roll of a human head in real time. It functions as a facial landmark detection library that identifies sixty-eight specific points on a face to enable precise orientation tracking from video streams. The system utilizes a high-performance inference engine to execute pre-trained machine learning models, ensuring that geometric analysis is performed efficiently on input frames. By mapping detected facial coordinates to a three-dimensional model, the software determines the camera-relative rotation of the head throu
This project is a computer vision library designed for facial landmark detection and alignment. It provides a framework for identifying and mapping specific points on a human face in both two-dimensional and three-dimensional space, enabling the normalization of facial geometry and orientation across diverse images. The system utilizes a deep learning approach to extract precise facial coordinates, supporting tasks such as expression analysis and geometric modeling. By employing a stacked hourglass architecture, the model performs multi-stage feature refinement to capture spatial relationship
PRNet es una librería de Python para la reconstrucción facial 3D. Utiliza un modelo de regresión de aprendizaje profundo para predecir la geometría facial 3D y los colores de los vértices a partir de una única imagen 2D de entrada para generar una malla texturizada.
Las características principales de yadiraf/prnet son: Single-Image 3D Reconstructions, Head Pose Estimation, Face Swapping, Face Swapping Frameworks, Facial Landmark Analysis, Facial Landmark Detection, Automated Blending, Computer Vision Research.
Las alternativas de código abierto para yadiraf/prnet incluyen: cleardusk/3ddfa — 3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… yinguobing/head-pose-estimation — This project is a computer vision tool designed to calculate the pitch, yaw, and roll of a human head in real time. It… 1adrianb/2d-and-3d-face-alignment — This project is a computer vision library designed for facial landmark detection and alignment. It provides a… aaronjackson/vrn — vrn is a 3D face reconstruction tool that generates three-dimensional volumetric representations of human faces from… xlite-dev/lite.ai.toolkit — lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of…