We propose a novel Transformer-based architecture for the task of generative
modelling of 3D human motion. Previous work commonly relies on RNN-based models
considering shorter forecast horizons reaching a stationary and often
implausible state quickly. Recent studies show that implicit temporal
representations in the frequency domain are also effective in making
predictions for a predetermined horizon. Our focus lies on learning
spatio-temporal representations autoregressively and hence generation of
plausible future developments over both short and long term. The proposed model
learns high dimensional embeddings for skeletal joints and how to compose a
temporally coherent pose via a decoupled temporal and spatial self-attention
mechanism. Our dual attention concept allows the model to access current and
past information directly and to capture both the structural and the temporal
dependencies explicitly. We show empirically that this effectively learns the
underlying motion dynamics and reduces error accumulation over time observed in
auto-regressive models. Our model is able to make accurate short-term
predictions and generate plausible motion sequences over long horizons. We make
our code publicly available at this https URL

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。