Few shot learning for creating videos from images
In prior chapters, we have seen how GANs can generate novel photorealistic images after being trained on a group of example photos. This technique can also be used to create variations of an image, either applying "filters" or new poses or angles of the base image. Extending this approach to its logical limit, could we create a "talking head" out of a single or a limited set of images? This problem is quite challenging – classical (or deep learning) approaches that apply "warping" transformations to a set of images create noticeable artifacts that degrade the realism of the output 13,14. An alternative approach is to use generative models to sample potential angular and positional variations of the input images (Figure 13.11), as performed by Zakharov et al. in their paper Few Shot Adversarial Learning of Realistic Neural Talking Head Models.15
Figure 13.11: Generative architecture for...
