Can I read Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis on EtoBox?

Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis by Briq, Rania; Zou, Chuhang; Pishchulin, Leonid; Broaddus, Chris; Gall, Juergen is a scholarly article available to read on EtoBox.

What is Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis about?

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.

Author

Briq, Rania; Zou, Chuhang; Pishchulin, Leonid; Broaddus, Chris; Gall, Juergen

Published

2022

Language

EN