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AlphaPose is a deep learning pose estimation framework and PyTorch computer vision library designed for detecting and tracking human body, face, hand, and foot keypoints in images and videos. It provides a system for skeletal posture estimation and multi-person pose tracking. The project implements tools for three-dimensional human pose reconstruction, generating joint positions and body mesh shapes from two-dimensional image data. It also includes a multi-person pose tracker capable of maintaining the identity of multiple people across consecutive video frames. The framework covers a broad
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
VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It functions as a toolkit for predicting parameters of the SMPL human body model to generate 3D mesh sequences. The system includes a 3D motion data exporter to convert predicted pose sequences into standard 3D file formats for use in graphics and animation software. It also provides a structured training pipeline for preparing datasets and training models to estimate body shapes from images. Its capabilities cover computer vision for estimating body pose and shape, as well as
OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot landmarks. It functions as a multi-person motion tracker, identifying the spatial coordinates of multiple individuals simultaneously within video streams or static images. Beyond two-dimensional detection, the software acts as a three-dimensional kinematics processor, reconstructing spatial movement data from single or multiple synchronized camera perspectives. The system distinguishes itself through a bottom-up approach that utilizes part-affinity fields to associate body parts across
VideoPose3D is a machine learning framework designed for 3D human pose estimation. It functions as a motion reconstruction tool that predicts 3D joint positions from 2D video sequences using a temporal convolutional network to process body movement over time.
The main features of facebookresearch/videopose3d are: 3D Pose Estimation, Motion Reconstruction, Semi-supervised Learning Pipelines, Temporal Convolutional Networks, Temporal Pose Analysis, Custom Video Inference, 3D Pose Reconstruction, Pose Estimation Validation.
Projects with overlapping indexed features include: mvig-sjtu/alphapose — AlphaPose is a deep learning pose estimation framework and PyTorch computer vision library designed for detecting and… open-mmlab/mmpose — MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D… mkocabas/vibe — VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It… cmu-perceptual-computing-lab/openpose — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot… leoxiaobin/deep-high-resolution-net.pytorch — This project is a PyTorch implementation of a research architecture designed for high-resolution representation… deeplabcut/deeplabcut — DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion…