Exploring the Effect of Sampling Strategy on Movement Generation with Generative Neural Networks

When using generative deep neural networks for creative applications it is common to explore multiple sampling approaches. This sampling stage is a crucial step, as choosing suitable sampling parameters can make or break the realism and perceived creative merit of the output. The process of selecting the correct sampling parameters is often task-specific and under-reported in many publications, which can make the reproducibility of the results challenging. We explore some of the most common sampling techniques in the context of generating human body movement, specifically dance movement, and attempt to shine a light on their advantages and limitations. This work presents a Mixture Density Recurrent Neural Network (MDRNN) trained on a dataset of improvised dance motion capture data from which it is possible to generate novel movement sequences. We outline several common sampling strategies for MDRNNs and examine these strategies systematically to further understand the effects of sampling parameters on motion generation. This analysis provides evidence that the choice of sampling strategy significantly affects the output of the model and supports the use of this model in creative applications. Building an understanding of the relationship between sampling parameters and creative machine-learning outputs could aid when deciding between different approaches in generation of dance motion and other creative applications.