We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results not from motion but variations of fundamental properties, such as rusting, decaying, melting, and weathering. To this end, we adopt the neural ordinary differential equation (ODE) to learn the underlying dynamics of appearance from a target exemplar. We simulate the ODE in two phases. At the "warm-up" phase, the ODE diffuses a random noise to an initial state. We then constrain the further evolution of this ODE to replicate the evolution of visual feature statistics in the exemplar during the generation phase. The particular innovation of this work is the neural ODE achieving both denoising and evolution for dynamics synthesis, with a proposed temporal training scheme. We study both relightable (BRDF) and non-relightable (RGB) appearance models. For both we introduce new pilot datasets, allowing, for the first time, to study such phenomena: For RGB we provide 22 dynamic textures acquired from free online sources; For BRDFs, we further acquire a dataset of 21 flash-lit videos of time-varying materials, enabled by a simple-to-construct setup. Our experiments show that our method consistently yields realistic and coherent results, whereas prior works falter under pronounced temporal appearance variations. A user study confirms our approach is preferred to previous work for such exemplars.

中文翻译：

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神经微分外观方程


我们提出了一种使用空间静止但时变的视觉统计数据来再现动态外观纹理的方法。虽然以前的大多数工作都将动态纹理分解为静态外观和运动，但我们关注的动态外观不是由运动引起的，而是基本特性的变化，例如生锈、腐烂、熔化和风化。为此，我们采用神经常微分方程 （ODE） 从目标样本中学习外观的潜在动力学。我们分两个阶段模拟 ODE。在“预热”阶段，ODE 将随机噪声扩散到初始状态。然后，我们限制此 ODE 的进一步演变，以在生成阶段复制示例中视觉特征统计的演变。这项工作的特殊创新是神经 ODE 实现了动力学合成的去噪和进化，并提出了一个时间训练方案。我们研究了可重新照明 （BRDF） 和不可重新照明 （RGB） 的外观模型。对于这两个数据集，我们引入了新的试点数据集，首次允许研究此类现象：对于 RGB，我们提供了从免费在线资源获取的 22 个动态纹理;对于 BRDF，我们进一步获取了一个包含 21 个时变材料的闪光灯视频的数据集，该数据集由易于构建的设置实现。我们的实验表明，我们的方法始终产生真实和连贯的结果，而以前的工作在明显的时间外观变化下步履蹒跚。一项用户研究证实，对于此类示例，我们的方法优于以前的工作。