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.

翻译：我们提出了一种方法，用于复现具有空间平稳但时变视觉统计特性的动态外观纹理。尽管大多数先前工作将动态纹理分解为静态外观和运动，我们专注于那些并非源于运动，而是由基本属性变化（如生锈、腐烂、融化和风化）所产生的动态外观。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态。我们分两个阶段模拟该ODE：在"预热"阶段，ODE将随机噪声扩散至初始状态；随后在生成阶段，我们约束该ODE的进一步演化，以复现样本中视觉特征统计量的演化过程。本工作的核心创新在于神经ODE能够同时实现去噪和演化以进行动态合成，并提出了相应的时序训练方案。我们研究了可重光照（BRDF）与不可重光照（RGB）两种外观模型。针对两者，我们均引入了新的先导数据集，首次实现了对此类现象的系统研究：对于RGB模型，我们提供了从公开网络资源采集的22种动态纹理；对于BRDF模型，我们进一步通过简易搭建的采集装置，获取了包含21种时变材质的闪光灯照明视频数据集。实验表明，我们的方法始终能产生逼真且连贯的结果，而现有方法在显著的时间外观变化下表现欠佳。用户研究证实，对于此类样本，我们的方法优于先前工作。