Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis
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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 the 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 dataset which we augment with ground-truth action labels. Experimental evaluation shows significant improvements in FID score and semantic consistency metrics compared to the state-of-the-art.
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