Real-time 3D human pose estimation on mobile devices is addressed by this repository, offering the official PyTorch implementation of the MobileHumanPose system. It targets researchers and developers in computer vision and mobile AI, enabling efficient 3D pose estimation directly on mobile platforms.

How It Works

This project implements a top-down approach for 3D multi-person pose estimation from single RGB images, leveraging custom backbone architectures within PyTorch. The system is designed for efficiency, facilitating real-time performance. It includes a pipeline for converting PyTorch models to TFLite for deployment on mobile devices.

Quick Start & Requirements

• Primary install / run command: pip install -r requirements.txt (within the main/ directory).
• Non-default prerequisites: PyTorch, CUDA, cuDNN, Anaconda, COCO API. Tested under Ubuntu 16.04, CUDA 11.2, Python 3.6.5, with NVIDIA RTX or V100 GPUs.
• Estimated setup time or resource footprint: Not specified, but requires substantial manual data downloading and structuring for datasets like Human3.6M, MPII, MS COCO 2017, MuCo-3DHP, and MuPoTS-3D. A dummy dataloader is available for faster PoC setup.
• Links: No direct links to official quick-start guides, documentation, or demo applications are provided in the README.

Highlighted Details

• Official PyTorch implementation of the MobileHumanPose paper (CVPRW 2021).
• Supports a range of public datasets including Human3.6M, MPII, MS COCO 2017, MuCo-3DHP, and MuPoTS-3D.
• Includes TFLite conversion for inference on mobile devices.
• Features custom backbone network implementations.

Maintenance & Community

The README notes anticipated "massive refactoring and optimization" and a new model release was expected by "end of December" (from a 2021 update), with the latest README revision in May 2022. This suggests potential for ongoing development but also indicates possible delays or a period of reduced activity. No explicit community channels (e.g., Discord, Slack) or roadmap links are provided.

Licensing & Compatibility

• License type: The license is not explicitly stated in the provided README.
• Compatibility notes: Due to the absence of a stated license, compatibility for commercial use or integration into closed-source projects is undetermined and presents a significant adoption blocker.

Limitations & Caveats

The project's current development status is unclear, with announcements of future work dating back to late 2021 and a README update in May 2022. A critical adoption blocker is the absence of a specified license, making commercial use impossible without clarification. Setup is complex, requiring manual data preparation and specific hardware (NVIDIA GPUs, CUDA). The code is tested on older Python versions (3.6.5) and specific CUDA versions, which may pose compatibility challenges with newer environments.