mlsanityData to 14 Oct 2026

Will You Ever Become Popular? Learning to Predict Virality of Dance Clips

arXiv 2111.03819 ↗First posted 6 Nov 2021cs.CV cs.MMcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 71 references that are in the map. First posted on arXiv 6 Nov 2021.

Builds on

34 of 71 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directionscited by 7
  2. 2026Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprintingcited by 0