Voxelpose-Pytorch

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Voxelpose-pytorch is an Open Source & Models tool that provides an official implementation for multi-camera 3D human pose estimation. It is designed for use in wild environments.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is voxelpose-pytorch?

Voxelpose-pytorch is the official PyTorch implementation of the research paper "VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment." This open-source tool enables researchers and developers to perform 3D human pose estimation using data from multiple cameras in uncontrolled environments. It includes detailed instructions for installation, data preparation using datasets like Shelf/Campus and CMU Panoptic, and guidance for training and evaluating models. The repository also provides pre-trained backbone models and camera parameters to facilitate immediate use and experimentation. It's a valuable resource for those working on advanced computer vision and human motion analysis.

Best used for

Ideal for developers and data scientists who need to implement multi-camera 3D human pose estimation, train models on datasets like CMU Panoptic, and evaluate performance in wild environments. Especially valuable for academic research and advanced computer vision applications.

Common actions

estimate 3D human pose

implement computer vision models

research multi-camera systems

deepfakeopen-sourceautomated workflowworkflowscollaborationlow-code/no-codegithub copilotface swapping"AI Agents"

Capabilities

Key features

Multi-camera 3D pose estimation
Wild environment support
PyTorch implementation
Data preparation scripts
Pre-trained models

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What datasets are supported by Voxelpose-pytorch for training and evaluation?

Voxelpose-pytorch supports the Shelf/Campus datasets for testing and the CMU Panoptic dataset for both training and evaluation. The repository provides instructions and processed camera parameters for these datasets, along with pre-trained models to facilitate immediate use.

Does Voxelpose-pytorch provide pre-trained models?

Yes, Voxelpose-pytorch provides a pre-trained backbone model (pose_resnet50_panoptic.pth.tar) which is a ResNet-50 model pretrained on COCO and finetuned jointly on Panoptic and MPII datasets. This allows users to quickly get started with the system.

What are the hardware requirements for training models with Voxelpose-pytorch?

The documentation indicates that models were trained on two GPUs, and users can specify GPU devices and batch size per GPU in the configuration files. This suggests that access to multiple GPUs is beneficial for efficient training, especially for larger datasets like CMU Panoptic.

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