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Human Motion Generation via Conditioned GMVAE with TUNet

Yongqi Liu,Jiashuang Zhou,Xiaoqin Du

Published 2024 in IEEE International Conference on Acoustics, Speech, and Signal Processing

ABSTRACT

In recent years, Variational Autoencoders (VAEs) have been proposed for motion synthesis to model action-label-conditioned human motion. However, these approaches only use Gaussian distribution as a prior assumption, this hard constraint might be too restrictive for the latent space and hurt the performance of the model. To address the issues, we model the latent space as a Gaussian mixture distribution and derive a new evidence lower bound (ELBO). Furthermore, to enhance the expressiveness of the model, we introduce Fisher discriminant as a regularization. We develop the attention mechanism and enable the Transformer-based U-Net to generate motions that correspond to semantic information only using action labels. The proposed CGMVAE-TU model has been evaluated on various datasets, and it surpasses the SOTA on almost all metrics. The generated human motions are realistic and natural.

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