论文标题
神经矢量场的浪费平面 - 控制动画
Wassersplines for Neural Vector Field--Controlled Animation
论文作者
论文摘要
大部分计算机生成的动画都是通过用钻机来操纵网格创建的。尽管这种方法可以很好地对动物（例如动物）进行动画化的态度，但它的灵活性有限，可以使结构化较低的自由形式对象进行动画化。我们介绍了Wassplines，这是一种基于连续标准化流量和最佳运输的最新进展，用于对非结构化密度进行动画的新型推理。关键思想是训练代表密钥帧之间运动的神经参数化速度场。然后，通过通过速度字段推进密钥帧来计算轨迹。我们解决了另一个Wasserstein Barycenter插值问题，以确保严格遵守关键框架。我们的工具可以通过各种基于PDE的正规化器来对轨迹进行风格化轨迹，从而创造出不同的视觉效果。我们在各种关键帧插值问题上演示了我们的工具，以制作时间连接动画而无需嵌入或索具。
Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. We introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in continuous normalizing flows and optimal transport. The key idea is to train a neurally-parameterized velocity field that represents the motion between keyframes. Trajectories are then computed by advecting keyframes through the velocity field. We solve an additional Wasserstein barycenter interpolation problem to guarantee strict adherence to keyframes. Our tool can stylize trajectories through a variety of PDE-based regularizers to create different visual effects. We demonstrate our tool on various keyframe interpolation problems to produce temporally-coherent animations without meshing or rigging.
