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Dance typically involves professional choreography with complex movements that follow a musical rhythm and can also be influenced by lyrical content.
Example-based automatic music-driven conventional dance motion synthesis
Rukun Fan, Songhua Xu, and Weidong Geng · 2011
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Music similarity-based approach to generating dance motion sequence
Minho Lee, Kyogu Lee, and Jaeheung Park · 2013
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Music content driven automated choreography with beat-wise motion connectivity constraints
Satoru Fukayama and Masataka Goto · 2015
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Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2015
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librosa: Audio and music signal analysis in python
Brian McFee, Colin Raffel, Dawen Liang, Daniel P Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto · 2015
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Groovenet: Real-time music-driven dance movement generation using artificial neural networks
Omid Alemi, Jules Françoise, and Philippe Pasquier · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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The dancing species: how moving together in time helps make us human
Kimerer LaMothe · 2019
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3d human pose estimation in video with temporal convolutions and semi-supervised training
Dario Pavllo, Christoph Feichtenhofer, David Grangier, and Michael Auli · 2019
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Modern dance choreography: Beyond the movement an analysis between lyrics and movement: Can identities be developed through modern dance choreography?
Hayley Elizabeth Powell · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Aist dance video database: Multi-genre, multi-dancer, and multi-camera database for dance information processing
Shuhei Tsuchida, Satoru Fukayama, Masahiro Hamasaki, and Masataka Goto · 2019
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On the continuity of rotation representations in neural networks
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li · 2019
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Motion capture from internet videos
Junting Dong, Qing Shuai, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, and Hujun Bao · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Learning to generate diverse dance motions with transformer
Jiaman Li, Yihang Yin, Hang Chu, Yi Zhou, Tingwu Wang, Sanja Fidler, and Hao Li · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Choreonet: Towards music to dance synthesis with choreographic action unit
Zijie Ye, Haozhe Wu, Jia Jia, Yaohua Bu, Wei Chen, Fanbo Meng, and Yanfeng Wang · 2020
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Choreomaster: choreography-oriented music-driven dance synthesis
Kang Chen, Zhipeng Tan, Jin Lei, Song-Hai Zhang, Yuan-Chen Guo, Weidong Zhang, and Shi-Min Hu · 2021
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Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano · 2022
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Groupdancer: Music to multi-people dance synthesis with style collaboration
Zixuan Wang, Jia Jia, Haozhe Wu, Junliang Xing, Jinghe Cai, Fanbo Meng, Guowen Chen, and Yanfeng Wang · 2022
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Music-to-dance generation with multiple conformer
Mingao Zhang, Changhong Liu, Yong Chen, Zhenchun Lei, and Mingwen Wang · 2022
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Music2dance: Dancenet for music-driven dance generation
Wenlin Zhuang, Congyi Wang, Jinxiang Chai, Yangang Wang, Ming Shao, and Siyu Xia · 2022
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Listen, denoise, action! audio-driven motion synthesis with diffusion models
Simon Alexanderson, Rajmund Nagy, Jonas Beskow, and Gustav Eje Henter · 2023
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Ai choreographer: Music conditioned 3d dance generation with aist++
Ruilong Li, Shan Yang, David A Ross, and Angjoo Kanazawa · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Transflower: probabilistic autoregressive dance generation with multimodal attention
Guillermo Valle-Pérez, Gustav Eje Henter, Jonas Beskow, Andre Holzapfel, Pierre-Yves Oudeyer, and Simon Alexanderson · 2021
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A bi-directional attention guided cross-modal network for music based dance generation
Di Fan, Lili Wan, Wanru Xu, and Shenghui Wang · 2022
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Danceformer: Music conditioned 3d dance generation with parametric motion transformer
Buyu Li, Yongchi Zhao, Shi Zhelun, and Lu Sheng · 2022
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Magic: Multi art genre intelligent choreography dataset and network for 3d dance generation
Ronghui Li, Junfan Zhao, Yachao Zhang, Mingyang Su, Zeping Ren, Han Zhang, and Xiu Li · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Diffusion-based co-speech gesture generation using joint text and audio representation
Anna Deichler, Shivam Mehta, Simon Alexanderson, and Jonas Beskow · 2023
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Music-driven group choreography
Nhat Le, Thang Pham, Tuong Do, Erman Tjiputra, Quang D Tran, and Anh Nguyen · 2023
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Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Edge: Editable dance generation from music
Jonathan Tseng, Rodrigo Castellon, and Karen Liu · 2023
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Multimodal dance style transfer
Wenjie Yin, Hang Yin, Kim Baraka, Danica Kragic, and Mårten Björkman · 2023
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T2m-gpt: Generating human motion from textual descriptions with discrete representations
Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Shaoli Huang, Yong Zhang, Hongwei Zhao, Hongtao Lu, and Xi Shen · 2023
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Avatargpt: All-in-one framework for motion understanding, planning, generation and beyond
Zixiang Zhou, Yu Wan, and Baoyuan Wang · 2023
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Motionbert: A unified perspective on learning human motion representations
Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu, Libin Liu, Wayne Wu, and Yizhou Wang · 2023
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