Few-shot generative model for skeleton-based human action synthesis using cross-domain adversarial learning

We propose few-shot generative models of skeleton-based human actions on limited samples of the target domain. We exploit large public datasets as a source of motion variations by introducing novel cross-domain and entropy regularization losses that effectively transfer the diversity of the motions contained in the source to the target domain. First, target samples are divided into patches, which are a set of short motion clips. For each patch, we search for a reference motion from the source dataset that is similar to the patch. Next, in adversarial training, our cross-domain regularization encourages the generated sequences to resemble the reference motion at the patch level. Entropy regularization prevents mode collapse by forcing the generator to follow the distribution of the source dataset. Experiments are performed on public datasets where we utilize three action classes from NTU RGB+D 120 as the target and all data of 60 action classes in NTU RGB+D as the source. Ten samples for each target action class, 30 in total, are selected as target data. The results demonstrate that data augmented with the proposed method improve recognition accuracy by 28 % using a ST-GCN classifier.