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.
引用
收藏
页数：15
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