MetaLayer: A Meta-Learned BSDF Model for Layered Materials

Author:

Guo Jie1,Li Zeru1,He Xueyan1,Wang Beibei2,Li Wenbin1,Guo Yanwen1,Yan Ling-Qi3

Affiliation:

1. State Key Lab for Novel Software Technology, Nanjing University, China

2. Nankai University and Nanjing University of Science and Technology, China

3. University of California, Santa Barbara, United States of America

Abstract

Reproducing the appearance of arbitrary layered materials has long been a critical challenge in computer graphics, with regard to the demanding requirements of both physical accuracy and low computation cost. Recent studies have demonstrated promising results by learning-based representations that implicitly encode the appearance of complex (layered) materials by neural networks. However, existing generally-learned models often struggle between strong representation ability and high runtime performance, and also lack physical parameters for material editing. To address these concerns, we introduce MetaLayer , a new methodology leveraging meta-learning for modeling and rendering layered materials. MetaLayer contains two networks: a BSDFNet that compactly encodes layered materials into implicit neural representations, and a MetaNet that establishes the mapping between the physical parameters of each material and the weights of its corresponding implicit neural representation. A new positional encoding method and a well-designed training strategy are employed to improve the performance and quality of the neural model. As a new learning-based representation, the proposed MetaLayer model provides both fast responses to material editing and high-quality results for a wide range of layered materials, outperforming existing layered BSDF models.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

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