Let’s all dance:Enhancing amateur dance motions  

作　　者：Qiu Zhou Manyi Li Qiong Zeng Andreas Aristidou Xiaojing Zhang Lin Chen Changhe Tu 

机构地区：[1]School of Computer Science&Technology,Shandong University,Qingdao 266000,China [2]School of Software,Shandong University,Jinan 250101,China [3]Department of Computer Science,University of Cyprus,Nicosia 1678,Cyprus [4]CYENS Centre of Excellence,Nicosia 1016,Cyprus [5]Qingdao Institute of Humanities and Social Sciences,Shandong University,Qingdao 266000,China.

出　　处：《Computational Visual Media》2023年第3期531-550,共20页计算可视媒体（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.62072284);Natural Science Foundation of Shandong Province(Grant No.ZR2021MF102);a Special Project of Shandong Province for Software Engineering(Grant No.11480004042015);internal funds from the University of Cyprus.

摘　　要：Professional dance is characterized by high impulsiveness,elegance,and aesthetic beauty.In order to reach the desired professionalism,it requires years of long and exhausting practice,good physical condition,musicality,but also,a good understanding of choreography.Capturing dance motions and transferring them to digital avatars is commonly used in the film and entertainment industries.However,so far,access to high-quality dance data is very limited,mainly due to the many practical difficulties in capturing the movements of dancers,making it prohibitive for largescale data acquisition.In this paper,we present a model that enhances the professionalism of amateur dance movements,allowing movement quality to be improved in both spatial and temporal domains.Our model consists of a dance-to-music alignment stage responsible for learning the optimal temporal alignment path between dance and music,and a dance-enhancement stage that injects features of professionalism in both spatial and temporal domains.To learn a homogeneous distribution and credible mapping between the heterogeneous professional and amateur datasets,we generate amateur data from professional dances taken from the AIST++dataset.We demonstrate the effectiveness of our method by comparing it with two baseline motion transfer methods via thorough qualitative visual controls,quantitative metrics,and a perceptual study.We also provide temporal and spatial module analysis to examine the mechanisms and necessity of key components of our framework.

关 键 词：ANIMATION music-to-motion alignment dance motion enhancement dance motion analysis 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]