Light does not only affect the colour of a scene.
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What makes the task easier, however, is that the autoencoders can focus on the facial expressions, rather than having to re-adapt to an entirely different bone structure, skin colour, and so on. This might seem trivial at first, but keep in mind that even morphing between images from the same person is far from being trivial. What is the simplest task that a face-swapping neural network can be asked to perform? Because it is built on autoencoders, is to reconstruct face A from face A.
The neural network works in a similar fashion, as a more complex mapping has to be learned. This is easy to understand, as more “changes” are necessary to convert A into B. The complexity of such a task largely depends on how different the two faces are. Imagine you had a picture of a person A, and had to manually edit it to make it look like person B. The task of the neural network is to encode an image from person A, and reconstruct one with similar features to resemble another person B. Let’s start by analysing the problem from a Machine Learning point of view.
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Since the release of face-swapping technology, there have been countless discussions online on how to train a network to achieve photorealistic results.
Training is the process which (literally!) trains a neural network to deconstruct and reconstruct faces. There are two critical processes involved in the making of any face-swap video: the training and the creation. Likewise, creating realistic face-swapped videos is hard. Like any artistic endeavour, the final result is a mixture of talent, commitment and right tools. Photoshop and After Effects are used every day by professionals, but that does not mean that just installing either of them is all it takes to create photorealistic images and videos.