How to Get Started with “Face Alignment in Full Pose Range: A 3D Total Solution”

If you are looking to explore the fascinating world of 3D face alignment, you’ve landed in the right place! This blog post will guide you through the process of setting up and using the “3DDFA” (3D Dense Face Alignment) library. We will also discuss some common troubleshooting tips to help you overcome any hurdles along the way.

Understanding the Concept: Face Alignment as a Puzzle

Imagine trying to fit together the pieces of a jigsaw puzzle, where each piece represents a facial feature. To complete the face, you need to align all the pieces accurately to capture the emotional essence of the image. The 3DDFA library serves a similar purpose; it aligns 3D face features from images for various applications—think of it as the master key to unlocking the intricacies of facial geometry.

Getting Started

To dive into the depths of 3D face alignment, you need to set up your environment and install the necessary prerequisites. Here’s how you can do that:

Requirements

• PyTorch = 0.4.1 (tested on v1.1.0 for macOS and Linux)
• Python = 3.6 (with Numpy, Scipy, Matplotlib)
• Dlib (optional for face and landmark detection)
• OpenCV (for image IO operations)
• Cython (for accelerating depth and PNCC render)
• Platform: Linux or macOS (not tested on Windows)

Installation Instructions

sudo pip3 install torch torchvision # For CPU version
sudo pip3 install numpy scipy matplotlib
sudo pip3 install dlib==19.5.0 # Choose the appropriate version for compatibility
sudo pip3 install opencv-python
sudo pip3 install cython

How to Use the Library

Follow these simple steps to utilize the 3D face alignment library:

1. Clone the Repository

git clone https://github.com/cleardusk/3DDFA.git 
cd 3DDFA

2. Download Pre-Trained Model

Download the Dlib landmark pre-trained model from Google Drive or Baidu Yun, and place it in the models directory.

3. Build Cython Module

cd utils/cython 
python3 setup.py build_ext -i

This step accelerates depth estimation and PNCC render as Python can be slow with loops.

4. Run the Main Program

python3 main.py -f samples/test1.jpg

You’ll know it’s successful when you see output logs in the terminal!

5. Additional Example

python3 main.py -f samples/emma_input.jpg --bbox_init=two --dlib_bbox=false

Inference Speed

Understanding the speed at which the model runs can be beneficial:

For CPU:

python3 speed_cpu.py

The average inference speed is around **14.50 ms**.

For GPU:

With a batch size of 128, the total inference time is approximately **34.7 ms**, averaging **0.27 ms per image**.

Troubleshooting Ideas

• Ensure that all prerequisites are correctly installed and that you are using the right version of Python and PyTorch.
• Check dependencies like Dlib and ensure they are compatible with PyTorch to avoid unnecessary runtime errors.
• If the repository takes too long to clone, ensure you have a stable internet connection or check GitHub’s status for outages.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that advancements like the 3D Dense Face Alignment are vital for the future of AI, enabling more comprehensive and effective solutions. Our team is continually exploring methodologies to push the envelope in artificial intelligence, benefiting our clients with the latest technological innovations.