"The training data used for the deep neural network was generated using Remedy Entertainment’s in-house capture pipeline based on a cutting edge commercial DI4D PRO system [Dimensional Imaging 2016] that employs nine video cameras."
You can always take tested ideas and apply to your own rig. The pioneering work was done by NVidia already, you see the results and it's up to you how much inspiration can you take from their work.
I don't think there's any mention of NN to create those filters - in fact, not sure if it's ML at all. Very likely some off-the-shelf CV face-detection - especially considering it must run real-time on a phone.
Even the "simplest" face detection algorithms rely on strong machine learning concepts, eg: Viola-Jones uses AdaBoost to train the face classifier cascade.
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[ 3.9 ms ] story [ 39.7 ms ] thread"The training data used for the deep neural network was generated using Remedy Entertainment’s in-house capture pipeline based on a cutting edge commercial DI4D PRO system [Dimensional Imaging 2016] that employs nine video cameras."
1. https://techcrunch.com/2015/09/15/snapchat-looksery/
https://www.kaggle.com/c/facial-keypoints-detection
"[...] It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. [...]"