If you’ve ever dreamed of stepping into a virtual reality space and seeing yourself seamlessly captured and transported there, Monoport is here to make that fantasy a reality! This guide will walk you through the steps to get started with Monoport, a remarkable system that captures a human body using just a single RGB webcam.

Requirements

To successfully run the Monoport system, you’ll need to prepare your environment by installing the necessary software:

• Python 3.7
• PyOpenGL 3.1.5 (requires X server in Ubuntu)
• PyTorch (tested on version 1.4.0)
• ImplicitSegCUDA
• human_inst_seg
• streamer_pytorch
• human_det

The system is optimized to run on two GeForce RTX 2080Ti GPUs.

Getting Started

Follow these steps to set up and run the Monoport demo:

1. Setup the Repository

Begin by downloading the model and installing the required dependencies:

sh scripts/download_model.sh
pip install -r requirements.txt

2. Start the Main Process

Now, it’s time to run the main process as a server. Depending on your input source (webcam, image folder, or video), choose the appropriate command to execute:

# For webcam input
python RTLmain.py --use_server --ip YOUR_IP_ADDRESS --port 5555 --camera --netG.ckpt_path .data/PIFunet_G --netC.ckpt_path .data/PIFunet_C

# For image folder input
python RTLmain.py --use_server --ip YOUR_IP_ADDRESS --port 5555 --image_folder IMAGE_FOLDER --netG.ckpt_path .data/PIFunet_G --netC.ckpt_path .data/PIFunet_C

# For video input
python RTLmain.py --use_server --ip YOUR_IP_ADDRESS --port 5555 --videos VIDEO_PATH --netG.ckpt_path .data/PIFunet_G --netC.ckpt_path .data/PIFunet_C

If everything goes smoothly, expect to see logs indicating successful initialization. Here’s a glimpse of what you might see:

loading networkG from .data/PIFunet_G ...
loading networkC from .data/PIFunet_C ...
initialize data streamer ...
Using cache found in homerui.cache/torchhub/NVIDIA_DeepLearningExamples/torchhub
* Serving Flask app 'main' (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
* Debug mode: on
* Running on http://YOUR_IP_ADDRESS:5555 (Press CTRL+C to quit)

3. Access the Server

To view the demo, open a web browser and navigate to http://YOUR_IP_ADDRESS:5555 from any device. You should see the **MonoPort VR Demo** page along with a screen on your desktop displaying the reconstructed output.

Troubleshooting Tips

If you encounter any issues during the setup or demo execution, here are some troubleshooting ideas:

• Ensure that your Python version is correct. Check with python --version.
• Verify that all required dependencies are installed successfully.
• Please ensure your GPU drivers are up to date.
• For installation issues related to any of the tools mentioned, file the issue in the corresponding GitHub repository.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With this guide, you should be on your way to experiencing the incredible world of monocular volumetric human teleportation. Just remember, think of Monoport like a magic mirror that captures your essence and transports it into a virtual realm! Happy teleporting!