论文标题
对人际和地面作用的深刻生成建模
Deep Generative Modelling of Human Reach-and-Place Action
论文作者
论文摘要
在3D空间中拾起并将物体放置的运动充满细节。通常，这些动作是由相同的约束形成的，以优化迅速，能源效率以及生理限制。然而，即使为了实现相同的目标，实现的运动始终会受到自然变化的影响。为了在计算上捕获这些方面，我们建议以开始和终端位置为条件的人覆盖和放置动作的深层生成模型。我们捕获了一个600个这样的人类3D动作的数据集，以对3D源和目标的2x3-D空间进行采样。虽然时间变化通常以复杂的学习机制（如复发性神经网络或具有内存或注意力）的网络进行建模，但我们在这里展示了一种更简单的方法，该方法是及时卷积的，并利用（周期性的）时间编码。该模型提供了一个潜在的代码并在起始和终端位置进行条件，在线性时间内生成完整的3D字符运动，作为一系列卷积。我们的评估包括几种消融，分析生成多样性和应用。
The motion of picking up and placing an object in 3D space is full of subtle detail. Typically these motions are formed from the same constraints, optimizing for swiftness, energy efficiency, as well as physiological limits. Yet, even for identical goals, the motion realized is always subject to natural variation. To capture these aspects computationally, we suggest a deep generative model for human reach-and-place action, conditioned on a start and end position.We have captured a dataset of 600 such human 3D actions, to sample the 2x3-D space of 3D source and targets. While temporal variation is often modeled with complex learning machinery like recurrent neural networks or networks with memory or attention, we here demonstrate a much simpler approach that is convolutional in time and makes use of(periodic) temporal encoding. Provided a latent code and conditioned on start and end position, the model generates a complete 3D character motion in linear time as a sequence of convolutions. Our evaluation includes several ablations, analysis of generative diversity and applications.
