论文标题
将运动表示为一系列潜在的原语，这是人类运动建模的灵活方法
Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling
论文作者
论文摘要
我们提出了人体运动的新表示形式，该运动用一系列潜在运动原语编码完整运动。最近，已经引入了任务通用运动先验，并提出了基于单个潜在代码的人类运动的连贯表示，对许多任务的结果令人鼓舞。将这些方法扩展到各种持续时间和帧速率的更长运动几乎是直接的，因为一个潜在代码证明了效率低下无法编码长期可变性。我们的假设是，长期动作比在一个块中更好地表示为一系列动作。通过利用序列到序列体系结构，我们提出了一个模型，该模型同时了解运动的时间分割，并在运动段上进行先验。为了通过时间分辨率和运动持续时间提供灵活性，我们的表示时间是连续的，可以在任何时间戳中查询。我们通过实验表明，我们的方法在稀疏点上的时空完成任务上对最先进的运动先验进行了显着改善。代码将在出版时提供。
We propose a new representation of human body motion which encodes a full motion in a sequence of latent motion primitives. Recently, task generic motion priors have been introduced and propose a coherent representation of human motion based on a single latent code, with encouraging results for many tasks. Extending these methods to longer motion with various duration and framerate is all but straightforward as one latent code proves inefficient to encode longer term variability. Our hypothesis is that long motions are better represented as a succession of actions than in a single block. By leveraging a sequence-to-sequence architecture, we propose a model that simultaneously learns a temporal segmentation of motion and a prior on the motion segments. To provide flexibility with temporal resolution and motion duration, our representation is continuous in time and can be queried for any timestamp. We show experimentally that our method leads to a significant improvement over state-of-the-art motion priors on a spatio-temporal completion task on sparse pointclouds. Code will be made available upon publication.
