Probabilistic dance performance alignment by fusion of multimodal features

This paper presents a probabilistic framework for the multimodal alignment of dance movements. The approach is based on a Hidden Markov Model (HMM) and considers different feature functions, each corresponding to a particular modality, namely motion features, extracted from depth maps, and audio features, extracted from audio recordings of dancers´ steps. We show that this approach allows performing accurate dancer alignment, while constituting a general framework for various multimodal alignment tasks.