Fake It Till You Recognize It: Quality Assessment for Human Action Generative Models.

Skeleton-based generative modelling is an important research topic to mitigate the heavy annotation process. In this work, we explore the impact of synthetic data on skeleton-based action recognition alongside its evaluation methods for more precise quality extraction. We propose a novel iterative weakly-supervised learning generative strategy for synthesising high-quality human actions. We combine conditional generative models with Bayesian classifiers to select the highest-quality samples. As an essential factor, we designed a discriminator network that, together with a Bayesian classifier relies on the most realistic instances to augment the amount of data available for the next iteration without requiring standard cumbersome annotation processes. Additionally, as a key contribution to assessing the quality of samples, we propose a novel measure based on human kinematics instead of employing commonly used evaluation methods, which are heavily based on images. The rationale is to capture the intrinsic characteristics of human skeleton dynamics, thereby complementing model comparison and alleviating the need to manually select the best samples. Experiments were carried out over four benchmarks of two well-known datasets (NTU RGB+D and NTU-120 RGB+D), where both our framework and model assessment can notably enhance skeleton-based action recognition and generation models by synthesising high-quality and realistic human actions.