论文标题
Gramgan：2D典范的深3D纹理合成
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
论文作者
论文摘要
我们提出了一种新颖的纹理合成框架，使得在2D示例图像下，能够产生无限的高质量3D纹理。受自然纹理合成的最新进展的启发，我们通过非线性结合学习的噪声频率来训练深层神经模型，从而产生纹理。为了实现以示例贴片为条件的高度现实输出，我们提出了一种新颖的损失功能，结合了风格转移和生成对抗网络的想法。特别是，我们训练合成网络以匹配歧视网络深度特征的革兰氏矩阵。此外，我们提出了两个建筑概念和一个外推策略，可显着提高泛化性能。特别是，我们通过学习扩展和偏置隐藏激活来将模型输入和条件注入隐藏的网络层。对各种示例的定量和定性评估激发了我们的设计决策，并表明我们的系统的表现优于以前的艺术状态。最后，我们进行了一项用户研究，以确认我们框架的好处。
We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies. To achieve a highly realistic output conditioned on an exemplar patch, we propose a novel loss function that combines ideas from both style transfer and generative adversarial networks. In particular, we train the synthesis network to match the Gram matrices of deep features from a discriminator network. In addition, we propose two architectural concepts and an extrapolation strategy that significantly improve generalization performance. In particular, we inject both model input and condition into hidden network layers by learning to scale and bias hidden activations. Quantitative and qualitative evaluations on a diverse set of exemplars motivate our design decisions and show that our system performs superior to previous state of the art. Finally, we conduct a user study that confirms the benefits of our framework.
