Summary
In this chapter, you explored controllable 3D-aware image synthesis using the GIRAFFE model. This model borrows concepts from NeRF, GANs, and 2D CNNs to create 3D scenes that are controllable. First, we had a refresher on GANs. Then, we dove deeper into the GIRAFFE model, how feature fields are generated, and how those feature fields are then transformed into RGB images. We then explored the outputs of this model and understood its properties and limitations. Finally, we briefly touched on how to train this model.
In the next chapter, we are going to explore a relatively new technique used to generate realistic human bodies in three dimensions called the SMPL model. Notably, the SMPL model is one of the small numbers of models that do not use deep neural networks. Instead, it uses more classical statistical techniques such as principal component analysis to achieve its objectives. You will learn the importance of good mathematical problem formulation in building models that...
