Real-Time Hair Filtering with Convolutional Neural Networks

Rendering of realistic-looking hair is in general still too costly to do real-time applications, from simulating the physics rendering fine details required for it look natural, including self-shadowing. We show how an autoencoder network, that can be evaluated real time, trained filter image few stochastic samples, self-shadowing, produce a much more detailed takes into account thickness and transparency.