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MVIG-SJTU avatar

MVIG-SJTU/AlphaPose

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mvig.org/research/alphapose.html↗

AlphaPose

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 range of computer vision capabilities, including multi-person keypoint localization, human motion tracking, and the reconstruction of three-dimensional body meshes.

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Features

  • Pose Estimation - Provides a deep learning framework for detecting and marking human body, face, hand, and foot keypoints for multiple people.
  • PyTorch Computer Vision Pipelines - Provides a comprehensive PyTorch-based computer vision pipeline for human pose estimation and motion tracking.
  • Human - Detects keypoints for the human body, face, hands, and feet across multiple people in images and videos.
  • Keypoint Detection - Locates specific anatomical points on the human body, face, hands, and feet for computer vision analysis.
  • Multi-Person Keypoint Localization - Finds and marks keypoints for the human body, face, hands, and feet across multiple people in images.
  • Multi-Person Trackers - Identifies and maintains the identity of multiple people across consecutive video frames using a pose tracking pipeline.
  • Top-Down Pipelines - Implements a top-down pipeline that detects human bounding boxes before predicting keypoints within them.
  • Pose Estimation Frameworks - Provides a deep learning system for detecting and tracking human body, face, hand, and foot keypoints in images and videos.
  • 3D Pose Estimation - Calculates three-dimensional joint positions and body mesh shapes from two-dimensional image data.
  • 3D Pose Reconstruction - Generates three-dimensional body representations and meshes from two-dimensional image data.
  • Graph Compiler Acceleration - Uses a dynamic computation graph and just-in-time compilation via Jittor to accelerate training and inference.
  • Multi-Stage Regression - Predicts keypoint locations through iterative refinement of confidence maps to increase spatial accuracy.
  • Region Proposal Filtering - Filters potential person detections using a regional proposal network to isolate individual human figures.
  • Temporal Pose Analysis - Matches detected skeletal poses across video frames by comparing spatial proximity and appearance features.
  • Human Motion Analysis - Monitors motion and trajectory by maintaining the identity of detected persons across video frames.
  • Video Pose Estimation - Matches poses from the same person across consecutive frames to ensure consistent identity in video sequences.
  • 2D-to-3D Mesh Lifting - Implements tools to generate three-dimensional joint positions and body mesh shapes from two-dimensional image data.
  • Real-Time Motion Tracking - Monitors human movement and identity across video frames by maintaining consistent skeletal posture tracking.
  • Computer Vision Libraries - System for real-time multi-person pose estimation and tracking.
  • Pose Estimation Frameworks - High-accuracy multi-person pose estimation system.
  • Real-time Estimation - High-accuracy multi-person pose estimation system.
8,583 stars·2,029 forks·Python·30 views

Star history

Star history chart for mvig-sjtu/alphaposeStar history chart for mvig-sjtu/alphapose

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does mvig-sjtu/alphapose do?

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.

What are the main features of mvig-sjtu/alphapose?

The main features of mvig-sjtu/alphapose are: Pose Estimation, PyTorch Computer Vision Pipelines, Human, Keypoint Detection, Multi-Person Keypoint Localization, Multi-Person Trackers, Top-Down Pipelines, Pose Estimation Frameworks.

Which projects share features with mvig-sjtu/alphapose?

Projects with overlapping indexed features include: cmu-perceptual-computing-lab/openpose — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot… open-mmlab/mmpose — MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D… zhec/realtime_multi-person_pose_estimation — This is a multi-person pose estimation framework designed for real-time human keypoint detection. It functions as a… mkocabas/vibe — VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It… facebookresearch/densepose — DensePose is a 3D human pose estimation framework designed to map 2D image pixels to a 3D surface-based model of the… tensorboy/pytorch_realtime_multi-person_pose_estimation — This project is a deep learning framework built for detecting and tracking human body keypoints in images and video…

Projects sharing features with AlphaPose

These projects share indexed features with AlphaPose. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • cmu-perceptual-computing-lab/openposeCMU-Perceptual-Computing-Lab avatar

    CMU-Perceptual-Computing-Lab/openpose

    34,145View on GitHub↗

    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

    C++caffecomputer-visioncpp
    View on GitHub↗34,145
  • open-mmlab/mmposeopen-mmlab avatar

    open-mmlab/mmpose

    7,374View on GitHub↗

    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

    Pythonanimal-pose-estimationbenchmarkcpm
    View on GitHub↗7,374
  • zhec/realtime_multi-person_pose_estimationZheC avatar

    ZheC/Realtime_Multi-Person_Pose_Estimation

    5,123View on GitHub↗

    This is a multi-person pose estimation framework designed for real-time human keypoint detection. It functions as a bottom-up human pose estimator that identifies skeletal joints across all people in a scene without requiring a separate person detector. The system utilizes a convolutional neural network model to generate heatmaps and vector fields for posture analysis. It specifically implements part affinity fields to encode the location and orientation of limbs, allowing the model to connect individual joints into complete skeletons. The project covers computer vision motion analysis and d

    Jupyter Notebookcaffecomputer-visioncpp11
    View on GitHub↗5,123
  • mkocabas/vibemkocabas avatar

    mkocabas/VIBE

    3,157View on GitHub↗

    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

    Python3d-human-pose3d-pose-estimationcvpr
    View on GitHub↗3,157
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