论文标题
高分辨率神经纹理合成具有远距离约束
High resolution neural texture synthesis with long range constraints
论文作者
论文摘要
质地合成领域在过去几年中见证了重要的进步，最著名的是使用卷积神经网络。但是，神经合成方法仍然难以再现大规模结构，尤其是具有高分辨率纹理的方法。为了解决此问题，我们首先引入一个简单的多分辨率框架，该框架有效地说明了长期依赖性。然后，我们表明，其他统计约束进一步改善了质地的繁殖，并具有很强的规律性。这可以通过约束神经网络的革兰氏阴矩阵和图像的功率谱来实现。另外，一个可能仅限于网络特征的自相关并删除革兰氏矩阵约束。在实验部分中，随后以无监督的方式和用户研究对所提出的方法进行了广泛的测试并与替代方法进行了比较。实验表明了高分辨率纹理的多尺度方案的兴趣，以及将其与常规纹理的其他约束结合在一起的兴趣。
The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis methods still struggle to reproduce large scale structures, especially with high resolution textures. To address this issue, we first introduce a simple multi-resolution framework that efficiently accounts for long-range dependency. Then, we show that additional statistical constraints further improve the reproduction of textures with strong regularity. This can be achieved by constraining both the Gram matrices of a neural network and the power spectrum of the image. Alternatively one may constrain only the autocorrelation of the features of the network and drop the Gram matrices constraints. In an experimental part, the proposed methods are then extensively tested and compared to alternative approaches, both in an unsupervised way and through a user study. Experiments show the interest of the multi-scale scheme for high resolution textures and the interest of combining it with additional constraints for regular textures.
