Human pose forecasting is a challenging problem involving complex human body motion and posture dynamics. In cases that there are multiple people in the environment, one's motion may also be influenced by the motion and dynamic movements of others. Although there are several previous works targeting the problem of multi-person dynamic pose forecasting, they often model the entire pose sequence as time series (ignoring the underlying relationship between joints) or only output the future pose sequence of one person at a time. In this paper, we present a new method, called Social Motion Transformer (SoMoFormer), for multi-person 3D pose forecasting. Our transformer architecture uniquely models human motion input as a joint sequence rather than a time sequence, allowing us to perform attention over joints while predicting an entire future motion sequence for each joint in parallel. We show that with this problem reformulation, SoMoFormer naturally extends to multi-person scenes by using the joints of all people in a scene as input queries. Using learned embeddings to denote the type of joint, person identity, and global position, our model learns the relationships between joints and between people, attending more strongly to joints from the same or nearby people. SoMoFormer outperforms state-of-the-art methods for long-term motion prediction on the SoMoF benchmark as well as the CMU-Mocap and MuPoTS-3D datasets. Code will be made available after publication.
翻译:人类的姿势预测是一个具有挑战性的问题,涉及复杂的人体运动和姿势动态。如果环境里有多个人,那么一个人的运动也可能受到其他人的运动和动态运动的影响。虽然以前有好几项针对多人动态的预测的工程,但他们往往将整个的姿势序列模拟成时间序列(显示联合之间的内在关系),或者仅仅输出未来一个人的顺序,在本文中,我们提出了一个新的方法,称为社会运动变异器(SoMoformer),用于多人3D的预测。我们的变异器结构将人类运动输入作为联合序列而不是一个时间序列来模拟。我们的变异器结构可以让我们关注联合体,同时预测每个联合体未来的运动序列。我们显示,随着时间序列的重新编排,SoMoformer自然地通过使用现场所有人组合作为输入查询而延伸到多人的场景。我们用学习的嵌入式来描述联合体、个人身份和全球位置,我们的模型将学习联合体与人之间的关系,以联合体- 而不是一个时间序列来模拟人类的模型,让我们关注联合体- 并更强烈地关注联合体- 将C- 和左右的模型作为共同的模型的模型的模型, 以同一或近境的模型的模型作为模型的模型的模型, 的模型作为共同的模型的模型的模型, 和模型,作为共同的模型的模型的模型,作为共同的模型的模型的模型的模型的模型的模型的模型, 和模型, 和模型,作为共同的模型,作为共同的模型的模型的模型,作为共同的模型,作为共同的模型,作为共同的模型的模型,作为共同的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型, 和和模型的模型的模型的模型的模型的模型的模型的模型, 和模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型, 和模型的模型的模型, 和模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的