Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
Models of human motion commonly focus either on trajectory prediction or
action classification but rarely both. The marked heterogeneity and intricate
compositionality of human motion render each task vulnerable to the data
degradation and distributional shift common to real-world scenarios. A
sufficiently expressive generative model of action could in theory enable data
conditioning and distributional resilience within a unified framework
applicable to both tasks. Here we propose a novel architecture based on
hierarchical variational autoencoders and deep graph convolutional neural
networks for generating a holistic model of action over multiple time-scales.
We show this Hierarchical Graph-convolutional Variational Autoencoder (HG-VAE)
to be capable of generating coherent actions, detecting out-of-distribution
data, and imputing missing data by gradient ascent on the model's posterior.
Trained and evaluated on H3.6M and the largest collection of open source human
motion data, AMASS, we show HG-VAE can facilitate downstream discriminative
learning better than baseline models.
Anthony Bourached, Robert Gray, Ryan-Rhys Griffiths, Ashwani Jha, Parashkev Nachev