We consider the problem of synthesizing multi-action human motion sequences
of arbitrary lengths. Existing approaches have mastered motion sequence
generation in single action scenarios, but fail to generalize to multi-action
and arbitrary-length sequences. We fill this gap by proposing a novel efficient
approach that leverages expressiveness of Recurrent Transformers and generative
richness of conditional Variational Autoencoders. The proposed iterative
approach is able to generate smooth and realistic human motion sequences with
an arbitrary number of actions and frames while doing so in linear space and
time. We train and evaluate the proposed approach on PROX and Charades
datasets, where we augment PROX with ground-truth action labels and Charades
with human mesh annotations. Experimental evaluation shows significant
improvements in FID score and semantic consistency metrics compared to the
state-of-the-art.

通过 Recurrent Transformers 和条件变分自编码器的结合，我们提出了一种新的迭代式方法，能够高效地生成具有任意动作序列的人类运动序列。该方法在 PROX 和 Charades 数据集上得到了验证，表明它在 FID 得分和语义一致性指标方面显著优于现有的技术方法。