Real-Time Hair Filtering with Convolutional Neural Networks

被引：0

作者：

Currius, Roc R. ^{[1
]}

Assarsson, Ulf ^{[1
]}

Sintorn, Erik ^{[1
]}

机构：

[1] Chalmers Inst Technol, Gothenburg, Sweden

来源：

PROCEEDINGS OF THE ACM ON COMPUTER GRAPHICS AND INTERACTIVE TECHNIQUES | 2022年 / 5卷 / 01期

基金：

瑞典研究理事会;

关键词：

hair; real-time; transparency; filtering; neural networks;

D O I：

10.1145/3522606

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

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页数：15

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