多特征超分辨网络在织物褶皱合成中的应用
Multi-feature super-resolution network for cloth wrinkle synthesis
摘要：
现有的物理织物模拟器存在计算量大、机械参数难以调整等问题。数据驱动方法提供了一种替代解决方案。它通常以低得多的计算成本合成布料动画，并且还创建高度类似于更可控的训练数据的抖动效果。在本文中，我们提出了一种基于深度学习的方法，用于合成具有高分辨率网格的布料动画。为此，我们首先创建一个用于训练的数据集：模拟一对低分辨率和高分辨率网格，并同步它们的运动。结果，两种网格表现出相似的大尺度变形，但不同的小褶皱。每个模拟网格对然后被转换成一对低分辨率和高分辨率的“图像”（一个2D样本阵列），每个样本可以被解释为三个特征中的任何一个：位移，法线和速度。利用这些图像对，我们设计了一个多特征超分辨率（MFSR）网络，该网络联合训练三个特征的上采样合成器。MFSR架构由两个关键组件组成：一个共享模块，它将多个特征作为输入，同时从相应的超分辨率任务中学习低级别表示;以及专注于各种高级语义的特定任务模块。由于所提出的基于运动学的损失函数，帧到帧的一致性得到了很好的保持。我们的方法在高帧速率下实现了逼真的结果：比传统的物理模拟快12-14倍。我们展示了我们的方法的性能与各种实验场景，包括一个穿着复杂的碰撞字符。
英文摘要：
Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors. Data-driven methods provide an alternative solution. It typically synthesizes cloth animation at a much lower computational cost, and also creates wrinkling effects that highly resemble the much controllable training data. In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes. To do this we first create a dataset for training: a pair of low and high resolution meshes are simulated and their motions are synchronized. As a result the two meshes exhibit similar large-scale deformation but different small wrinkles. Each simulated mesh pair are then converted into a pair of low and high resolution "images" (a 2D array of samples), with each sample can be interpreted as any of three features: the displacement, the normal and the velocity. With these image pairs, we design a multi-feature super-resolution (MFSR) network that jointly train an upsampling synthesizer for the three features. The MFSR architecture consists of two key components: a sharing module that takes multiple features as input to learn low-level representations from corresponding super-resolution tasks simultaneously; and task-specific modules focusing on various high-level semantics. Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function. Our method achieves realistic results at high frame rates: 12-14 times faster than traditional physical simulation. We demonstrate the performance of our method with various experimental scenes, including a dressed character with sophisticated collisions.
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