Build Face Capture and avatar 3D Driven 1. **Choose the Right Environment**: Set up your development environment with Python and the necessary libraries, including OpenCV, TensorFlow, Dlib, Mediapipe, Blender, or

Maya. 2. **Face Detection**: - Use a pretrained model like Haar Cascades from OpenCV or FaceNet from TensorFlow to detect faces in images or video frames. - Capture frames from your video source. 3. **Facial Landmarks Detection**: - Utilize a library like Dlib or Mediapipe to identify facial landmarks (eyes, nose, mouth, etc.) on the detected face. - Extract the coordinates of these landmarks. 4. **3D Character Animation Setup**: - Create or import a 3D character model into Blender or Maya. - Familiarize yourself with the API of your chosen 3D software for programmatically controlling the model. 5. **Mapping Facial Landmarks to 3D Model**: - Determine which facial landmarks will control specific movements or expressions of your 3D character model. - Create a mapping between the 2D landmarks detected on the face and the control points on the 3D model. 6. **Python Script Integration**: - Write a Python script to tie everything together. This script should: - Capture video frames and detect faces using your chosen face detection model. - Extract facial landmarks using Dlib or Mediapipe. - Map the extracted 2D landmarks to the 3D model's control points using PyBlendFacial or MayaPythonAPI. - Animate the 3D character based on the mapped landmarks. - Render the frames with the character's animation. - Save the frames as an animated video. 7. **Testing and Optimization**: - Test your script with different video sources and scenarios to ensure it works as expected. - Fine-tune the mapping and animation to achieve the desired results. 1/3 6. **Python Script Integration**: Write a Python script to tie everything together. This script should: - Capture video frames and detect faces using your chosen face detection model. - Extract facial landmarks using Dlib or Mediapipe. - Map the extracted 2D landmarks to the 3D model's control points using PyBlend Facial or Maya PythonAPI. - Animate the 3D character based on the mapped landmarks. - Render the frames with the character's animation. - Save the frames as an animated video. 7. **Testing and Optimization**: - Test your script with different video sources and scenarios to ensure it works as expected. - Fine-tune the mapping and animation to achieve the desired results. - Experiment with different face detection and landmarks models to improve accuracy. - Optimize your code and algorithms for better performance. 8. **Training and Fine-Tuning**: If needed, you can train and fine-tune your face detection and landmarks models on custom datasets to improve their accuracy for your specific use case. 9. **Documentation and Maintenance**: - Document your workflow and code for future reference. - Ensure that your project is well-maintained and can be easily updated as needed. 2/3