Dance2MIDI: Dance-driven multi-instrument music generation

Abstract

AbstractDance-driven music generation aims to generate musical pieces conditioned on dance videos. Previous works focus on monophonic or raw audio generation, while the multi-instrument scenario is under-explored. The challenges associated with dance-driven multi-instrument music (MIDI) generation are twofold: (i) lack of a publicly available multi-instrument MIDI and video paired dataset and (ii) the weak correlation between music and video. To tackle these challenges, we have built the first multi-instrument MIDI and dance paired dataset (D2MIDI). Based on this dataset, we introduce a multi-instrument MIDI generation framework (Dance2MIDI) conditioned on dance video. Firstly, to capture the relationship between dance and music, we employ a graph convolutional network to encode the dance motion. This allows us to extract features related to dance movement and dance style. Secondly, to generate a harmonious rhythm, we utilize a transformer model to decode the drum track sequence, leveraging a cross-attention mechanism. Thirdly, we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task. A BERT-like model is employed to comprehend the context of the entire music piece through self-supervised learning. We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.