Thomas Chambon
ABSTRACT
Our objective is to compute a textural loss that can be used to train texture generators with multiple material channels typically used for physically based rendering such as albedo, normal, roughness, metalness, ambient occlusion, etc. Neural textural losses often build on top of the feature spaces of pretrained convolutional neural networks. Unfortunately, these pretrained models are only available for 3-channel RGB data and hence limit neural textural losses to this format. To overcome this limitation, we show that passing random triplets to a 3-channel loss provides a multi-channel loss that can be used to generate high-quality material textures.
Supplemental Material
Available for Download
- Miika Aittala, Timo Aila, and Jaakko Lehtinen. 2016. Reflectance Modeling by Neural Texture Synthesis. ACM Trans. Graph. 35, 4, Article 65 (2016), 13 pages.Google Scholar
Digital Library
- Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2015. Texture Synthesis Using Convolutional Neural Networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1(NIPS’15). 262–270.Google Scholar
- Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming Hsuan Yang. 2017. Diversified texture synthesis with feed-forward networks. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 266–274.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations.Google Scholar
- Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky. 2016. Texture Networks: Feed-Forward Synthesis of Textures and Stylized Images. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48(ICML’16). 1349–1357.Google Scholar
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