Developed a comprehensive end-to-end pipeline that processes input images, utilizes a pretrained StyleGAN2 model to project them into the latent space, and generates a smooth morphing video between facial images through latent space interpolation.
- Utilized the state-of-the-art StyleGAN2 generative model to produce realistic and high-quality face morphing videos.
- Automated facial landmark detection and cropping using a well-established, pretrained neural network library, enhancing input image compatibility with the StyleGAN2 model.
- Implemented a seamless pipeline that can be executed on Google Colab, offering an accessible platform with GPU support for widespread use.
- Upgrade to dlib's 68-point facial landmark model for better alignment of the input images and more visually aesthetic morphing videos.
- Local implementation of this pipeline with MPS (Apple Metal) acceleration vs. using Google Colab's cloud platform.