Real-Time Hair Filtering with Convolutional Neural Networks

Roc R. Currius, Ulf Assarsson, Erik Sintorn

Symposium on Interactive 3D Graphics and Games (I3D '22)


Rendering of realistic-looking hair is in general still too costly to do in real-time applications, from simulating the physics to rendering the fine details required for it to look natural, including self-shadowing.

We show how an autoencoder network, that can be evaluated in real time, can be trained to filter an image of few stochastic samples, including self-shadowing, to produce a much more detailed image that takes into account real hair thickness and transparency.


The paper can be found at

You can download the author version of the paper from here.

Supplemental Files


The presentation will be available through the I3D youtube channel after the conference.

Bib. Reference

    author = {Currius, R. R. and Assarsson, U. and Sintorn, E.},
    title = {Real-Time Hair Filtering with Convolutional Neural Networks},
    booktitle = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
    doi = {10.1145/3522606},
    year = {2022}
    issue_date = {May 2022},
    journal = {Proc. ACM Comput. Graph. Interact. Tech.},
    volume = {5},
    number = {1},
    articleno = {15},