Multiview-Human-Pose-Estimation-Pytorch

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multiview-human-pose-estimation-pytorch is an Open Source & Models tool that provides an official PyTorch implementation for 3D human pose estimation. It uses a cross-view fusion approach for accurate results.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is multiview-human-pose-estimation-pytorch?

multiview-human-pose-estimation-pytorch is an official PyTorch implementation of the "Cross View Fusion for 3D Human Pose Estimation, ICCV 2019" paper. This open-source project enables researchers and developers to perform 3D human pose estimation using multiple camera views. The repository includes detailed instructions for installation, dependency management, and data preparation for datasets like MPII and Human36M. It supports both 2D and 3D pose estimation, with options for training on mixed datasets and testing on H36M using either CPU or GPU versions. The project is ideal for those working on computer vision tasks related to human motion analysis and 3D reconstruction.

Best used for

Ideal for developers and data scientists who need to implement and experiment with advanced 3D human pose estimation models, train on mixed datasets, and test on standard benchmarks. Especially valuable for researchers in computer vision and those developing applications requiring precise human motion analysis.

Common actions

estimate human pose

implement computer vision

analyze human motion

open-sourceworkflowscollaboration"AI Agents"low-code/no-codegithub copilotautomated workflowdeepfakeface swapping

Capabilities

Key features

3D human pose estimation
Cross-view fusion
PyTorch implementation
MPII data support
Human36M data support
CPU/GPU testing

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of data is required for this pose estimation tool?

The tool requires data from datasets like MPII Human Pose Dataset and Human36M. Instructions are provided for preparing these datasets, including converting annotation files to JSON format and organizing image files, potentially in zip archives.

Can I train and test both 2D and 3D human pose estimation models?

Yes, the repository supports both 2D and 3D human pose estimation. You can train 2D models on mixed datasets (MPII+H36M) and test 3D models on H36M, with separate scripts for CPU and GPU versions of 3D estimation.

What are the hardware requirements for running this implementation?

While specific hardware requirements are not detailed, the implementation provides options for both CPU and GPU-based 3D pose estimation. For optimal performance, especially during training or 3D GPU testing, a capable GPU is recommended.

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