Real-Time Hair Filtering with Convolutional Neural Networks

Roc R. Currius, Ulf Assarsson, Erik Sintorn

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

Abstract

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.

Paper

The paper can be found at https://doi.org/10.1145/3522606.

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

Supplemental Files

Presentation

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

Bib. Reference


@inproceedings{RealTimeHairFilteringCNN:currius:2022,
    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},
}

 


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