Audio-visual learning, aimed at exploiting the relationship between audio and visual
modalities, has drawn considerable attention since deep learning started to be used …

Driving 3D characters to dance following a piece of music is highly challenging due to the
spatial constraints applied to poses by choreography norms. In addition, the generated …

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with
FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion …

Dancing to music is one of human's innate abilities since ancient times. In machine learning
research, however, synthesizing dance movements from music is a challenging problem …

The creation of improvised dancing choreographies is an important research field of cross-
modal analysis. A key point of this task is how to effectively create and correlate music and …

When coming up with phrases of movement, choreographers all have their habits as they
are used to their skilled dance genres. Therefore, they tend to return certain patterns of the …

Music-driven choreography is a challenging problem with a wide variety of industrial
applications. Recently, many methods have been proposed to synthesize dance motions …

Synthesize human motions from music (ie, music to dance) is appealing and has attracted
lots of research interests in recent years. It is challenging because of the requirement for …

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and
other media using AI algorithms. Due to its wide range of applications and the demonstrated …

Dance and music are two highly correlated artistic forms. Synthesizing dance motions has
attracted much attention recently. Most previous works conduct music-to-dance synthesis via …