Care sunt principalele funcționalități ale mvig-sjtu/alphapose?

Principalele funcționalități ale mvig-sjtu/alphapose sunt: Pose Estimation, PyTorch Computer Vision Pipelines, Human, Keypoint Detection, Multi-Person Keypoint Localization, Multi-Person Trackers, Top-Down Pipelines, Pose Estimation Frameworks.

Care sunt câteva alternative open-source pentru mvig-sjtu/alphapose?

Alternativele open-source pentru mvig-sjtu/alphapose includ: 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…

AlphaPose

AlphaPose este un framework de deep learning pentru estimarea posturii și o bibliotecă PyTorch de computer vision, concepută pentru detectarea și urmărirea punctelor cheie ale corpului uman, feței, mâinilor și picioarelor în imagini și clipuri video. Oferă un sistem pentru estimarea scheletică a posturii și urmărirea posturii pentru mai multe persoane.

Proiectul implementează instrumente pentru reconstrucția tridimensională a posturii umane, generând poziții ale articulațiilor și forme ale mesh-ului corporal din date bidimensionale. Include, de asemenea, un tracker de postură pentru mai multe persoane, capabil să mențină identitatea acestora pe parcursul cadrelor video consecutive.

Framework-ul acoperă o gamă largă de capabilități de computer vision, inclusiv localizarea punctelor cheie pentru mai multe persoane, urmărirea mișcării umane și reconstrucția mesh-urilor corporale 3D.

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.

Istoric stele

MVIG-SJTUAlphaPose

Name: mvig-sjtu/alphapose
Author: MVIG-SJTU

View on GitHub

8,583 stele2,029 fork-uriPython12 vizualizărimvig.org/research/alphapose.html

AlphaPose

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.

Alternative open-source pentru AlphaPose

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu AlphaPose.

cmu-perceptual-computing-lab/openpose
CMU-Perceptual-Computing-Lab/openpose
34,145Vezi pe 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
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open-mmlab/mmpose
open-mmlab/mmpose
7,374Vezi pe 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
Vezi pe GitHub7,374
zhec/realtime_multi-person_pose_estimation
ZheC/Realtime_Multi-Person_Pose_Estimation
5,123Vezi pe 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
Vezi pe GitHub5,123
mkocabas/vibe
mkocabas/VIBE
3,157Vezi pe 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
Vezi pe GitHub3,157

Vezi toate cele 30 alternative pentru AlphaPose

Întrebări frecvente

Ce face mvig-sjtu/alphapose?

AlphaPose

Features

Istoric stele

AlphaPose

Features

Alternative open-source pentru AlphaPose

CMU-Perceptual-Computing-Lab/openpose

open-mmlab/mmpose

ZheC/Realtime_Multi-Person_Pose_Estimation

mkocabas/VIBE

Întrebări frecvente

Istoric stele

Alternative open-source pentru AlphaPose

CMU-Perceptual-Computing-Lab/openpose

open-mmlab/mmpose

ZheC/Realtime_Multi-Person_Pose_Estimation

mkocabas/VIBE

Întrebări frecvente