3D Face Model Generator
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This project is aimed at creating 3D models from short facial videos. By combining advanced computer vision, deep learning techniques, and photogrammetry tools, this project allows users to upload a video of their face and generate a lifelike 3D avatar. The application of this technology spans various industries such as virtual reality, augmented reality, gaming, medical research, and more.
Processing Pipeline
To bring 2D video footage into the world of 3D, a robust pipeline was developed to process video data and reconstruct faces in three dimensions. This process involves several key stages, each contributing to the accuracy and quality of the final 3D model:
Extracting Frames from Video
The process begins by breaking down a short video of a face into individual frames—essentially snapshots of the face from different angles. These frames provide multiple perspectives, which are needed to construct the 3D shape.
Preprocessing the Images
The extracted frames often need adjustments before they can be used for 3D modeling. The preprocessing involves:
- White Balance and Contrast Adjustment: Brightness, contrast, and color balance are automatically enhanced to make facial features more distinct.
- Background Removal: GrabCut algorithm is used to separate the person’s face from the background, ensuring that the focus is entirely on the facial features.
Detecting Key Facial Features
Once the images are processed, MediaPipe, a powerful machine learning tool, is used to detect hundreds of facial landmarks, such as the eyes, nose, mouth, and jawline. These landmarks act as markers, defining the structure of the face in detail.
Matching Features Across Frames
With facial landmarks detected in each frame, the next step is to match corresponding points from one frame to the next. This process ensures that the same facial features (such as the tip of the nose) are identified across multiple angles of the face. Accurate matching is crucial for reconstructing the 3D shape.
Building the 3D Model
Using the matched points, the depth and spatial relationships between them are calculated. This allows the 2D images to be converted into a 3D point cloud, where each point represents a tiny section of the face in space.
Creating the Final 3D Face
After generating the 3D point cloud, the details are smoothed to make the model more lifelike. This process fills in any gaps and enhances the overall appearance of the face, resulting in a realistic 3D mesh that captures the unique features of the individual’s face.
Future Directions
There is potential for improvement in future 3D face modeling by further refining the 3D mesh quality through more advanced algorithms and by increasing the density of the facial landmarks.
