Visual language transformer framework for multimodal dance performance evaluation and progression monitoring

Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals’ sensorimotor skills and motion analysis. Recent studi...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 30649 - 22
Hlavný autor:	Chen, Lei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 20.08.2025 Nature Publishing Group Nature Portfolio
Predmet:	639/705/1042 639/705/117 639/705/258 639/705/794 Adaptability Choreography Classification Dance Dance performance monitoring Dancers & choreographers Dancing - physiology Deep learning Genre Graph convolutional network Humanities and Social Sciences Humans Language Learning Literature reviews Long short-term memory Methods multidisciplinary Multimedia Multimodal analysis Music Neural Networks, Computer Performance assessment Performance evaluation Psychomotor Performance Rhythm Science Science (multidisciplinary) Sensorimotor system Synchronization Transformer Deep learning Multimodal analysis Graph convolutional network Transformer Dance performance monitoring
ISSN:	2045-2322, 2045-2322
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Abstract	Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals’ sensorimotor skills and motion analysis. Recent studies on dance performance assessment primarily focus on evaluating simple dance movements using a single task, typically estimating final performance scores. We propose a novel transformer-based visual-language framework for multi-modal dance performance evaluation and progression monitoring. Our approach addresses two core challenges: the learning of feature representations for complex dance movements synchronized with music across diverse styles, genres, and expertise levels, and capturing the multi-task nature of dance performance evaluation. To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. Our model evaluates three key tasks: (i) multilabel dance classification, (ii) dance quality estimation, and (iii) dance-music synchronization, leveraging primitive-based segmentation and multi-modal inputs. During the pre-training phase, we utilize contrastive loss to capture primitive-based features from complex dance motion and music data. For downstream tasks, we propose a transformer-based text prompting approach to conduct multi-task evaluations for the three assessment objectives. Our model outperforms in diverse downstream tasks. For multilabel dance classification, our model achieves a score of 75.20, representing a 10.25% improvement over CotrastiveDance, on the dance quality estimation task, the proposed model achieved a 92.09% lower loss on CotrastiveDance. For dance-music synchronization, our model excels with a score of 2.52, outperforming CotrastiveDance by 48.67%.
AbstractList	Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals' sensorimotor skills and motion analysis. Recent studies on dance performance assessment primarily focus on evaluating simple dance movements using a single task, typically estimating final performance scores. We propose a novel transformer-based visual-language framework for multi-modal dance performance evaluation and progression monitoring. Our approach addresses two core challenges: the learning of feature representations for complex dance movements synchronized with music across diverse styles, genres, and expertise levels, and capturing the multi-task nature of dance performance evaluation. To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. Our model evaluates three key tasks: (i) multilabel dance classification, (ii) dance quality estimation, and (iii) dance-music synchronization, leveraging primitive-based segmentation and multi-modal inputs. During the pre-training phase, we utilize contrastive loss to capture primitive-based features from complex dance motion and music data. For downstream tasks, we propose a transformer-based text prompting approach to conduct multi-task evaluations for the three assessment objectives. Our model outperforms in diverse downstream tasks. For multilabel dance classification, our model achieves a score of 75.20, representing a 10.25% improvement over CotrastiveDance, on the dance quality estimation task, the proposed model achieved a 92.09% lower loss on CotrastiveDance. For dance-music synchronization, our model excels with a score of 2.52, outperforming CotrastiveDance by 48.67%. Abstract Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals’ sensorimotor skills and motion analysis. Recent studies on dance performance assessment primarily focus on evaluating simple dance movements using a single task, typically estimating final performance scores. We propose a novel transformer-based visual-language framework for multi-modal dance performance evaluation and progression monitoring. Our approach addresses two core challenges: the learning of feature representations for complex dance movements synchronized with music across diverse styles, genres, and expertise levels, and capturing the multi-task nature of dance performance evaluation. To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. Our model evaluates three key tasks: (i) multilabel dance classification, (ii) dance quality estimation, and (iii) dance-music synchronization, leveraging primitive-based segmentation and multi-modal inputs. During the pre-training phase, we utilize contrastive loss to capture primitive-based features from complex dance motion and music data. For downstream tasks, we propose a transformer-based text prompting approach to conduct multi-task evaluations for the three assessment objectives. Our model outperforms in diverse downstream tasks. For multilabel dance classification, our model achieves a score of 75.20, representing a 10.25% improvement over CotrastiveDance, on the dance quality estimation task, the proposed model achieved a 92.09% lower loss on CotrastiveDance. For dance-music synchronization, our model excels with a score of 2.52, outperforming CotrastiveDance by 48.67%. Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals' sensorimotor skills and motion analysis. Recent studies on dance performance assessment primarily focus on evaluating simple dance movements using a single task, typically estimating final performance scores. We propose a novel transformer-based visual-language framework for multi-modal dance performance evaluation and progression monitoring. Our approach addresses two core challenges: the learning of feature representations for complex dance movements synchronized with music across diverse styles, genres, and expertise levels, and capturing the multi-task nature of dance performance evaluation. To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. Our model evaluates three key tasks: (i) multilabel dance classification, (ii) dance quality estimation, and (iii) dance-music synchronization, leveraging primitive-based segmentation and multi-modal inputs. During the pre-training phase, we utilize contrastive loss to capture primitive-based features from complex dance motion and music data. For downstream tasks, we propose a transformer-based text prompting approach to conduct multi-task evaluations for the three assessment objectives. Our model outperforms in diverse downstream tasks. For multilabel dance classification, our model achieves a score of 75.20, representing a 10.25% improvement over CotrastiveDance, on the dance quality estimation task, the proposed model achieved a 92.09% lower loss on CotrastiveDance. For dance-music synchronization, our model excels with a score of 2.52, outperforming CotrastiveDance by 48.67%.Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content. Research in automatic dance performance assessment has the potential to enhance individuals' sensorimotor skills and motion analysis. Recent studies on dance performance assessment primarily focus on evaluating simple dance movements using a single task, typically estimating final performance scores. We propose a novel transformer-based visual-language framework for multi-modal dance performance evaluation and progression monitoring. Our approach addresses two core challenges: the learning of feature representations for complex dance movements synchronized with music across diverse styles, genres, and expertise levels, and capturing the multi-task nature of dance performance evaluation. To achieve this, we integrate contrastive self-supervised learning, spatiotemporal graph convolutional networks (STGCN), long short-term memory networks (LSTM), and transformer-based text prompting. Our model evaluates three key tasks: (i) multilabel dance classification, (ii) dance quality estimation, and (iii) dance-music synchronization, leveraging primitive-based segmentation and multi-modal inputs. During the pre-training phase, we utilize contrastive loss to capture primitive-based features from complex dance motion and music data. For downstream tasks, we propose a transformer-based text prompting approach to conduct multi-task evaluations for the three assessment objectives. Our model outperforms in diverse downstream tasks. For multilabel dance classification, our model achieves a score of 75.20, representing a 10.25% improvement over CotrastiveDance, on the dance quality estimation task, the proposed model achieved a 92.09% lower loss on CotrastiveDance. For dance-music synchronization, our model excels with a score of 2.52, outperforming CotrastiveDance by 48.67%.
ArticleNumber	30649
Author	Chen, Lei
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References	S Persine (16345_CR14) 2022; 94 16345_CR35 Y Zhong (16345_CR8) 2024; 38 16345_CR12 16345_CR17 A Davis (16345_CR30) 2018; 37 16345_CR19 16345_CR18 16345_CR7 ZK Abdul (16345_CR28) 2022; 10 16345_CR9 16345_CR20 16345_CR22 16345_CR21 K Chen (16345_CR1) 2021; 40 P Khosla (16345_CR34) 2020; 33 16345_CR4 16345_CR3 16345_CR6 16345_CR5 G Rekik (16345_CR15) 2024; 41 16345_CR24 16345_CR23 16345_CR26 16345_CR25 16345_CR29 B Li (16345_CR13) 2022; 36 16345_CR31 16345_CR11 SV Jaque (16345_CR2) 2020; 11 16345_CR10 16345_CR32 H Mukhtar (16345_CR16) 2023; 168 Z Wu (16345_CR27) 2020; 32 F Dai (16345_CR33) 2020; 8
References_xml	– volume: 38 start-page: 10270 year: 2024 ident: 16345_CR8 publication-title: Proc. AAAI Conf. Artif. Intell. – ident: 16345_CR18 doi: 10.1109/DICTA51227.2020.9363408 – ident: 16345_CR12 doi: 10.1007/978-3-031-19827-4_41 – volume: 10 start-page: 122136 year: 2022 ident: 16345_CR28 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3223444 – ident: 16345_CR32 doi: 10.1145/3485664 – ident: 16345_CR35 doi: 10.1109/CVPR42600.2020.00975 – ident: 16345_CR6 doi: 10.1109/CVPR52688.2022.01077 – ident: 16345_CR4 doi: 10.1109/CVPR52688.2022.00296 – ident: 16345_CR26 doi: 10.25080/Majora-7b98e3ed-003 – ident: 16345_CR21 doi: 10.1126/scirobotics.abm6010 – ident: 16345_CR10 doi: 10.1609/aaai.v32i1.12328 – ident: 16345_CR29 – ident: 16345_CR5 doi: 10.1145/3394171.3413560 – ident: 16345_CR19 doi: 10.1109/CVPR42600.2020.00986 – ident: 16345_CR9 – volume: 41 start-page: 1093 year: 2024 ident: 16345_CR15 publication-title: Chronobiol. Int. doi: 10.1080/07420528.2024.2379579 – ident: 16345_CR17 doi: 10.1201/9781003338888 – volume: 168 start-page: 363 year: 2023 ident: 16345_CR16 publication-title: Neural Netw. doi: 10.1016/j.neunet.2023.09.047 – ident: 16345_CR20 doi: 10.1145/3394171.3413932 – volume: 8 start-page: 53215 year: 2020 ident: 16345_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2980891 – volume: 33 start-page: 18661 year: 2020 ident: 16345_CR34 publication-title: Adv. Neural Inf. Process. Syst. – ident: 16345_CR31 doi: 10.1109/ICCV48922.2021.01315 – volume: 37 start-page: 1 year: 2018 ident: 16345_CR30 publication-title: ACM Trans. Graph. (TOG) doi: 10.1145/3197517.3201371 – ident: 16345_CR22 – volume: 36 start-page: 1272 year: 2022 ident: 16345_CR13 publication-title: Proc. AAAI Conf. Artif. Intell. – ident: 16345_CR24 – volume: 11 start-page: 1000 year: 2020 ident: 16345_CR2 publication-title: Front. Psychol. doi: 10.3389/fpsyg.2020.01000 – volume: 94 start-page: 105619 year: 2022 ident: 16345_CR14 publication-title: Clin. Biomech. doi: 10.1016/j.clinbiomech.2022.105619 – volume: 32 start-page: 4 year: 2020 ident: 16345_CR27 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.2978386 – ident: 16345_CR23 doi: 10.1145/3474085.3475307 – ident: 16345_CR7 doi: 10.1109/ICASSP49357.2023.10096824 – volume: 40 start-page: 1 year: 2021 ident: 16345_CR1 publication-title: ACM Trans. Graph. (TOG) – ident: 16345_CR25 doi: 10.1109/ICASSP49357.2023.10094628 – ident: 16345_CR11 doi: 10.1109/ICCV.2017.236 – ident: 16345_CR3 doi: 10.1109/CVPR46437.2021.00940
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Snippet	Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and content.... Abstract Dance is often perceived as complex due to the need for coordinating multiple body movements and precisely aligning them with musical rhythm and...
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Title	Visual language transformer framework for multimodal dance performance evaluation and progression monitoring
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