  Dance challenges are going viral in video communities like TikTok nowadays.
Once a challenge becomes popular, thousands of short-form videos will be
uploaded in merely a couple of days. Therefore, virality prediction from dance
challenges is of great commercial value and has a wide range of applications,
such as smart recommendation and popularity promotion. In this paper, a novel
multi-modal framework which integrates skeletal, holistic appearance, facial
and scenic cues is proposed for comprehensive dance virality prediction. To
model body movements, we propose a pyramidal skeleton graph convolutional
network (PSGCN) which hierarchically refines spatio-temporal skeleton graphs.
Meanwhile, we introduce a relational temporal convolutional network (RTCN) to
exploit appearance dynamics with non-local temporal relations. An attentive
fusion approach is finally proposed to adaptively aggregate predictions from
different modalities. To validate our method, we introduce a large-scale viral
dance video (VDV) dataset, which contains over 4,000 dance clips of eight viral
dance challenges. Extensive experiments on the VDV dataset demonstrate the
efficacy of our model. Extensive experiments on the VDV dataset well
demonstrate the effectiveness of our approach. Furthermore, we show that short
video applications like multi-dimensional recommendation and action feedback
can be derived from our model.
