Projects sharing features with PRNet

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

vrn is a 3D face reconstruction tool that generates three-dimensional volumetric representations of human faces from single two-dimensional images. It utilizes a volumetric convolutional neural network regression model to predict 3D volume data directly from image pixels. The system converts these volumetric predictions into 3D meshes through isosurface extraction and vertex coloring. It further applies realistic surface details by mapping two-dimensional image pixels onto the resulting 3D mesh using nearest-neighbor texture projection. The project provides capabilities for single-image dept

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

Created by Wayne Wu at Tsinghua University.

DeepFaceLab is a deep learning software suite designed for face swapping and the creation of deepfake videos. It functions as a neural network image compositor that replaces human faces or entire heads in video files to produce synthetic media. The tool provides capabilities for digital facial manipulation, including the ability to modify the perceived age of people in video sequences. It uses automated pattern recognition to blend source faces onto target frames to create seamless visual composites. The system covers a broad technical surface including landmark-based face alignment, autoenc

SimSwap is a deep learning face swapping framework and computer vision media processor built with PyTorch. It functions as an image synthesis tool designed to replace a person's identity in images and videos with a target face using a single trained model. The system operates as a video identity replacement tool that swaps identities across frames while preserving the original expressions and lighting of the source media. It enables digital identity manipulation and the production of synthetic media through automated facial feature mapping. The framework supports both the application of trai

Pigo is a computer vision library written in Go for locating human faces in images and video streams. It provides tools for face detection, facial landmark identification, and pupil and eye localization. The project is implemented in pure Go to ensure portable execution without external dependencies. It supports compilation to WebAssembly, enabling face detection and image processing to run directly in web browsers without a backend. The library's capabilities include real-time face detection using classifier cascades and gaze tracking localization. It maps anatomical points on the face to a

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

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

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

This project is a deep learning framework designed for facial landmark detection. It functions as a computer vision library that provides the necessary routines to locate precise points on human faces within images by generating spatial probability maps. The architecture distinguishes itself through high-resolution parallel branching, which maintains detailed visual representations throughout the entire network. By utilizing multi-scale feature fusion, the model repeatedly exchanges information across these parallel streams to integrate fine-grained spatial details with broader semantic conte

PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo

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

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

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 project is a PyTorch implementation of a research architecture designed for high-resolution representation learning. It serves as a computer vision framework focused on precise keypoint detection, human pose estimation, and semantic image segmentation. The implementation provides specialized tools for identifying anatomical landmarks on the human body and predicting facial keypoint coordinates to analyze orientation and alignment. It utilizes a system of multi-resolution parallel streams and repeated multi-scale fusion to maintain high-resolution representations throughout the network.

Avatarify-python is a real-time face animation tool that uses a PyTorch-based neural network to map facial movements from a live camera feed onto a static image. It creates photorealistic animated avatars that mimic a user's movements for use in video software. The project includes a remote GPU inference client that offloads heavy computational workloads to a remote server, allowing high-performance animations to run on low-spec hardware. It also features a virtual webcam driver to route synthetic video streams into video conferencing applications as a standard camera device. The system prov

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

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.evoLVe is a deep learning library designed for the training and deployment of facial recognition models. It provides a comprehensive framework for converting facial images into numerical feature vectors, enabling identity verification and similarity analysis across large-scale datasets. The project facilitates the entire lifecycle of facial analysis, from dataset preparation and image standardization to distributed model training. It includes utilities for detecting facial landmarks and applying geometric transformations to ensure consistent input orientation, as well as data augmentatio

Pulse is a face image super-resolution tool and self-supervised image enhancer. It functions as a generative model image upsampler and latent space optimization tool designed to increase photo resolution and recover image details. The system differentiates itself by using latent space exploration and spherical constraints to find high-fidelity matches within a generative model. It employs geodesic distance measurement and spherical latent space optimization to regularize representations and maintain parameter radii during the recovery process. The project covers facial image restoration thro

The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.

GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and tetrahedral meshes. It functions as an image-to-3D reconstructor and text-to-3D generator, utilizing a differentiable 3D renderer to produce realistic visual perspectives and material effects. The system enables the creation of 3D assets from single 2D images, point clouds, or descriptive text prompts. It features a latent space interpolator for creating smooth transitions between different 3D objects and supports the independent control of geometry and texture. The project cov

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

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

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

TripoSR is a single-image 3D reconstruction system that generates a high-quality textured mesh from one photograph in under half a second. It uses a feedforward neural network to process a single image through a transformer architecture, compressing the input into a compact latent vector that conditions the entire reconstruction pipeline. The system outputs a separate UV texture map with configurable resolution, replacing vertex colors for higher-quality surface detail. The project is built around an end-to-end differentiable pipeline that trains the entire reconstruction system from image in