In this paper, we propose a novel architecture for the task of 3D human motion modelling. We argue that the problem can be interpreted as a generative modelling task: A network learns the conditional synthesis of human poses where the model is conditioned on a seed sequence. Our focus lies on the generation of plausible future developments over longer time horizons, whereas previous work considered shorter time frames of up to 1 second. To mitigate the issue of convergence to a static pose, we propose a novel architecture that leverages the recently proposed self-attention concept. The task of 3D motion prediction is inherently spatio-temporal and thus the proposed model learns high dimensional joint embeddings followed by a decoupled temporal and spatial self-attention mechanism. The two attention blocks operate in parallel to aggregate the most informative components of the sequence to update the joint representation. This allows the model to access past information directly and to capture spatio-temporal dependencies explicitly. We show empirically that this reduces error accumulation over time and allows for the generation of perceptually plausible motion sequences over long time horizons as well as accurate short-term predictions. Accompanying video available at https://youtu.be/yF0cdt2yCNE .

本文提出一种新颖的基于Transformer的架构，用于生成3D人体运动的模型。该模型能够学习高维度骨骼关节的嵌入，通过解耦的时间和空间自注意机制使得生成的姿态在时间上是连贯的。实验结果表明该模型能够准确地生成短期预测并在长期时间跨度内生成合理的运动序列。

用于三维人体运动预测的时空转换器