Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) from a single image with unknown lighting and geometry. However, most existing networks only consider per-pixel losses which limit their capability to recover local features such as smooth glossy regions. A few generative adversarial networks use multiple discriminators for different parameter maps, increasing network complexity. We present a novel end-to-end generative adversarial network (GAN) to recover appearance from a single picture of a nearly-flat surface lit by flash. We use a single unified adversarial framework for each parameter map. An attention module guides the network to focus on details of the maps. Furthermore, the SVBRDF map loss is combined to prevent paying excess attention to specular highlights. We demonstrate and evaluate our method on both public datasets and real data. Quantitative analysis and visual comparisons indicate that our method achieves better results than the state-of-the-art in most cases.
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The authors would like to thank Jie Guo from Nanjing University for his kind help with the comparison. Ying Song was partially supported by the National Natural Science Foundation of China (No. 61602416) and Shaoxing Science and Technology Plan Project (No. 2020B41006).
The authors have no competing interests to declare that are relevant to the content of this article.
Zeqi Shi is a master student at the School of Information Science and Technology of Zhejiang Sci-Tech University. He received his B.S. degree from Zhejiang Sci-Tech University in 2019. His research interests include deep learning and computer graphics.
Xiangyu Lin is a lecturer in the School of Information Science and Technology of Zhejiang Sci-Tech University. He obtained his B.S. and Ph.D. degrees in electronic information technology and instruments from Zhejiang University. His main research interests are image processing and machine learning.
Ying Song is an associate professor in the School of Information Science and Technology of Zhejiang Sci-Tech University. She obtained her B.S. and Ph.D. degrees in computer science and technology from Zhejiang University. Her main research interests are appearance modeling and realistic rendering.
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Shi, Z., Lin, X. & Song, Y. An attention-embedded GAN for SVBRDF recovery from a single image. Comp. Visual Media 9, 551–561 (2023). https://doi.org/10.1007/s41095-022-0289-1
- spatially-varying bidirectional reflectance distribution function (SVBRDF)
- appearance capture
- generative adversarial network (GAN)
- attention mechanism