标题： 一种用于随机3D人体运动预测的时空连续网络 显示英文标题

标题： A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction

摘要： 随机人类运动预测（HMP）由于其广泛的应用而受到越来越多的关注。 尽管生成领域取得了快速进展，现有方法在学习连续时间动态和预测随机运动序列方面仍面临挑战。 它们往往忽视复杂人类运动中固有的灵活性，并容易出现模式崩溃。 为缓解这些问题，我们提出了一种名为STCN的新方法，用于随机和连续的人类运动预测，该方法包括两个阶段。 具体来说，在第一阶段，我们提出了一种时空连续网络，以生成更平滑的人类运动序列。 此外，创新性地将锚点集引入随机HMP任务，以防止模式崩溃，这指的是潜在的人类运动模式。 在第二阶段，STCN借助锚点集努力获取观察到的运动序列的高斯混合分布（GMM）。 它还关注每个锚点的概率，并采用从每个锚点采样多个序列的策略，以缓解人类运动中的类内差异。 在两个广泛使用的数据集（Human3.6M和HumanEva-I）上的实验结果表明，我们的模型在多样性和准确性方面都表现出色。 显示英文摘要

摘要： Stochastic Human Motion Prediction (HMP) has received increasing attention due to its wide applications. Despite the rapid progress in generative fields, existing methods often face challenges in learning continuous temporal dynamics and predicting stochastic motion sequences. They tend to overlook the flexibility inherent in complex human motions and are prone to mode collapse. To alleviate these issues, we propose a novel method called STCN, for stochastic and continuous human motion prediction, which consists of two stages. Specifically, in the first stage, we propose a spatio-temporal continuous network to generate smoother human motion sequences. In addition, the anchor set is innovatively introduced into the stochastic HMP task to prevent mode collapse, which refers to the potential human motion patterns. In the second stage, STCN endeavors to acquire the Gaussian mixture distribution (GMM) of observed motion sequences with the aid of the anchor set. It also focuses on the probability associated with each anchor, and employs the strategy of sampling multiple sequences from each anchor to alleviate intra-class differences in human motions. Experimental results on two widely-used datasets (Human3.6M and HumanEva-I) demonstrate that our model obtains competitive performance on both diversity and accuracy.