PoseEstimationForMobile

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PoseEstimationForMobile is a Coding & Development tool that provides real-time single-person pose estimation for Android and iOS. It uses CPM and Hourglass models with TensorFlow for efficient mobile inference.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is PoseEstimationForMobile?

PoseEstimationForMobile is an open-source project designed for real-time single-person pose estimation on Android and iOS devices. It leverages CPM and Hourglass models, implemented with TensorFlow, and incorporates inverted residuals (MobileNet V2) for optimized, real-time inference. The repository includes code for training both CPM and Hourglass models, along with demo source code for Android and iOS. This allows developers to integrate pose estimation capabilities into their mobile applications with high performance. The project provides pre-trained models and detailed instructions for setting up training environments, converting models for mobile deployment (Mace, TFLite, CoreML), and benchmarking performance across various mobile chipsets.

Best used for

Ideal for developers who need to integrate real-time single-person pose estimation into Android and iOS applications, train custom pose estimation models, and benchmark performance on mobile devices. Especially valuable for creating efficient and responsive AI-powered mobile experiences.

Common actions

implement pose estimation

develop mobile AI

optimize deep learning

"AI Agents"github copilotface swappingautomated workflowlow-code/no-codeworkflowsdeepfakecollaborationopen-source

Capabilities

Key features

Real-time pose estimation
Android demo code
iOS demo code
CPM model
Hourglass model
TensorFlow implementation
MobileNet V2 integration

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What models are implemented in PoseEstimationForMobile?

PoseEstimationForMobile currently implements the CPM (Convolutional Pose Machines) and Hourglass models. These models are built using TensorFlow and are optimized for real-time inference on mobile devices by utilizing inverted residuals (MobileNet V2) instead of standard convolutions.

Can I train my own models with this repository?

Yes, the repository provides the necessary code for training both CPM and Hourglass models. It supports Python3 and TensorFlow >= 1.4, and includes configurations for hyper-parameters. You can use your own datasets or the provided AI Challenger dataset for training.

How can I deploy the models on mobile devices?

The project supports deployment on Android and iOS. For Android, models can be converted to Mace or TFLite formats. For iOS, models can be converted to CoreML. The repository includes detailed instructions and demo source code for integrating these converted models into mobile applications.

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Open Source & Models Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs

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This tool also appears in

Content & Design › 3D & Animation AI Agents & Automation › AI Frameworks & Infra

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