Open-source alternatives to 3DDFA

This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

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 an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D images into 3D content by estimating monocular depth and synthesizing missing color and depth data to fill occluded regions. The system uses a layered depth image representation to manage scene boundaries and pixel connectivity. This allows for novel view synthesis, enabling the generation of videos that simulate motion parallax effects from different camera perspectives. The project covers a range of spatial modeling capabilities, including depth map estimation, disparity-based

OpenFace is an affective computing framework and facial behavior analysis toolkit designed for the extraction of facial features and the recognition of muscle movements to analyze human emotional behavior. It provides a research platform for analyzing human emotion and social interaction patterns through computer vision. The software implements a suite of tools for detecting facial landmarks, calculating head pose in 3D space relative to a camera, and tracking eye gaze by analyzing eye position and orientation. It also includes capabilities for facial action unit recognition to identify speci

Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images. It provides specialized pipelines for text-to-3D generation and image-to-3D reconstruction, utilizing a neural radiance field trainer to produce geometry and textures. The framework is distinguished by its support for hybrid geometry backends, including signed distance functions, tetrahedra grids, and volume grids. It employs score distillation sampling to guide the generation process and features a modular plugin system for loading custom modules and nodes. The system covers

face-api.js is a TensorFlow.js face recognition library and browser-based computer vision API. It provides tools for performing face detection, recognition, and landmark prediction within browsers and Node.js. The library includes a biometric identity descriptor generator that creates numerical vectors to compare identity and similarity between images. It features a facial landmark detection tool for mapping sixty-eight specific coordinate points on a face, as well as an age and gender estimation model. Its capabilities cover real-time facial analysis, including the recognition of facial exp

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

MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D keypoints on humans, animals, and faces. It serves as a computer vision model zoo and a framework for both 2D pose estimation and 3D pose lifting. The project is distinguished by its modular architecture and extensibility, employing a registry-based system and hierarchical configurations to allow for custom algorithm integration and model pipeline customization. It supports diverse estimation paradigms, including top-down, bottom-up, and two-stage pose lifting workflows. The

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

facenet-pytorch is a facial recognition library for PyTorch that provides pretrained neural networks for detecting faces and extracting facial embeddings. It includes an MTCNN face detector for locating faces and landmarks, alongside an InceptionResnet face encoder to convert facial images into high-dimensional vectors for identity verification. The project provides tools for identity recognition by comparing facial embeddings using cosine similarity. It also supports facial video tracking to maintain identity consistency across consecutive frames and allows for the fine-tuning of pretrained

This library provides a deep learning framework for identifying human faces and extracting facial landmarks within digital images. It utilizes a multi-task convolutional neural network architecture to simultaneously perform face classification, bounding box regression, and landmark localization. The system processes images through three sequential stages of neural networks, incorporating image pyramid resizing to detect faces of varying scales. To ensure accuracy, it employs bounding box regression to refine coordinate predictions and non-maximum suppression to filter out redundant overlappin

Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a modular pipeline that handles the entire lifecycle of facial processing, including detection, geometric alignment, and the transformation of facial images into high-dimensional numerical vector embeddings for identity verification and similarity comparison. The library distinguishes itself through a model ensemble approach, which combines predictions from multiple pre-trained neural networks to improve classification accuracy and reduce bias. It also integrates advanced security fe

Face-recognition.js is a computer vision software development kit for Node.js that provides tools for detecting, mapping, and identifying human faces within images and video streams. It functions as a bridge to high-performance native libraries, enabling developers to perform complex facial analysis tasks directly within JavaScript and TypeScript environments. The library distinguishes itself by combining deep learning inference with geometric landmark mapping. It utilizes pre-trained neural networks to extract facial feature vectors and employs Euclidean distance calculations to determine th

opencv4nodejs is a set of JavaScript wrappers and a C++ native addon that provides Node.js bindings for the OpenCV library. It functions as a computer vision library and image processing framework, exposing high-performance C++ algorithms to a JavaScript environment. The project enables the execution of vision algorithms for detecting faces, tracking objects, and analyzing visual data using deep neural networks. It includes capabilities for data pattern classification, text pattern recognition, and the identification of facial landmarks and gestures. The framework covers a broad capability s

CompreFace is a facial recognition system designed for human face detection, identification, and biometric identity verification. It provides a registry of known people and the ability to match faces in images against this database to determine a specific identity. The system extracts facial landmarks to map geometry and analyzes physical attributes including age, gender, and head pose. It can also verify whether two different images belong to the same individual. The project is implemented as a microservice-based deployment utilizing a REST API gateway and a PostgreSQL metadata store. It in

This is an official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. The project was formerly referred by RingNet. The codebase consists of the inference code, i.e. give an face image using this code one can generate a 3D mesh of a…

This project implements some basic functions related to 3D faces.

This project is created by Tu Xiaoguang (xguangtu@outlook.com) and Luo Yao (luoyao_alpha@outlook.com). Any questions pls open issues for our project, we will reply quickly.

SolveSpace is a parametric CAD software and 3D solid modeler. It functions as a constraint-based geometry solver that allows for the creation of 2D and 3D models where positions and sizes are driven by geometric rules and mathematical relationships. The project distinguishes itself by integrating a mechanical linkage simulator for analyzing the motion of pin and ball joints and a G-code generator that produces CNC machining instructions with cutter radius compensation. It also includes tools for kinematic simulation, allowing users to trace the motion paths of components and export coordinate

This project provides a compact neural network architecture designed for human face detection and facial landmark localization. It functions as a specialized computer vision tool that identifies faces and extracts five specific facial key points within a single inference pass, making it suitable for integration into resource-constrained environments. The system utilizes a lightweight convolutional backbone and an anchor-based detection mechanism to maintain a small memory footprint while performing real-time processing. By employing a multi-task learning head, the model simultaneously predict

Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

CadQuery is a programmatic 3D modeling library and parametric CAD framework that allows for the generation of complex geometric solids and assemblies using a fluent Python API. It functions as a B-Rep geometry engine, enabling the construction of models through code rather than a graphical user interface. The project is built on the Open CASCADE Technology kernel and utilizes a method-chaining API to link geometric commands in sequence. It distinguishes itself through a workplane-based coordinate system and a powerful selection system that uses topological and spatial filtering to target spec

Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w

Wonder3D is a diffusion-based system for single image 3D reconstruction. It generates high-detail 3D meshes from a single input image by producing consistent multi-view normal maps and color images. The pipeline functions as a multi-view normal map generator and a textured mesh extractor. It uses cross-domain multi-view synthesis to create view-dependent maps, which are then converted into 3D geometry through radiance fusion and memory-efficient surface reconstruction. The project covers 3D mesh generation, multi-view generation, and textured 3D modeling. It also includes capabilities for tr

ml-sharp is a neural radiance field framework designed for single-image 3D reconstruction. It uses a neural network to predict 3D geometry and appearance from a single photograph in a single feedforward pass. The system generates metric 3D scene representations and includes a real-time view synthesizer for producing high-resolution images of new viewpoints. It also features a camera trajectory renderer that creates video sequences by moving a virtual camera through the predicted 3D space. The project covers coordinate-based neural rendering, 3D Gaussian representation regression, and real-ti