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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: ACM Transactions on Multimedia Computing, Communications, and Applications

سال: 2022

ISSN: ['1551-6857', '1551-6865']

DOI: https://doi.org/10.1145/3477533