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Autoregressive models excel in modeling sequential dependencies by enforcing causal constraints, yet they struggle to capture complex bidirectional patterns due to their unidirectional nature.
“Momask: Generative masked modeling of 3d human motions,”
Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng, · 1910
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“The kit whole-body human motion database,”
Christian Mandery, Ömer Terlemez, Martin Do, Nikolaus Vahrenkamp, and Tamim Asfour, · 2015
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“Pixel recurrent neural networks,”
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu, · 2016
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“Wavenet: A generative model for raw audio,”
Aaron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu, et al., · 2016
Earlier work this paper cites.
“The kit motion-language dataset,”
Matthias Plappert, Christian Mandery, and Tamim Asfour, · 2016
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“Neural discrete representation learning,”
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu, · 2017
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“Attention is all you need,”
A Vaswani, · 2017
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“Efficient neural audio synthesis,”
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron Oord, Sander Dieleman, and Koray Kavukcuoglu, · 2018
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“Bert: Pre-training of deep bidirectional transformers for language understanding,” 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2019
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“Xlnet: Generalized autoregressive pretraining for language understanding,”
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le, · 2019
Earlier work this paper cites.
“Amass: Archive of motion capture as surface shapes,”
Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black, · 2019
Earlier work this paper cites.
“Language models are few-shot learners,”
Tom B Brown, · 2020
Earlier work this paper cites.
“Structured denoising diffusion models in discrete state-spaces,”
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg, · 2021
Cited alongside, same era.
“Soundstream: An end-to-end neural audio codec,”
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi, · 2021
Cited alongside, same era.
“Learning transferable visual models from natural language supervision,”
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., · 2021
Cited alongside, same era.
“Temos: Generating diverse human motions from textual descriptions,”
Mathis Petrovich, Michael J Black, and Gül Varol, · 2022
Cited alongside, same era.
“Generating diverse and natural 3d human motions from text,”
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng, · 2022
Cited alongside, same era.
“Motiondiffuse: Text-driven human motion generation with diffusion model,”
“Diffusion motion: Generate text-guided 3d human motion by diffusion model,”
Zhiyuan Ren, Zhihong Pan, Xin Zhou, and Le Kang, · 2023
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“Executing your commands via motion diffusion in latent space,”
Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, and Gang Yu, · 2023
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“Make-an-animation: Large-scale text-conditional 3d human motion generation,”
Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh, and Sonal Gupta, · 2023
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“Generating human motion from textual descriptions with discrete representations,”
Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, and Ying Shan, · 2023
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“Attt2m: Text-driven human motion generation with multi-perspective attention mechanism,”
Chongyang Zhong, Lei Hu, Zihao Zhang, and Shihong Xia, · 2023
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Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu, · 2022
Cited alongside, same era.
“Motionclip: Exposing human motion generation to clip space,”
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or, · 2022
Cited alongside, same era.
“Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts,”
Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng, · 2022
Cited alongside, same era.
“Autoregressive diffusion models,”
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans, · 2022
Cited alongside, same era.
“Cmu graphics lab motion capture database,”
CMU Graphics Lab, · 2022
Cited alongside, same era.
“Human motion diffusion model,” 2022
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano, · 2022
Cited alongside, same era.
“Text2avatar: Text to 3d human avatar generation with codebook-driven body controllable attribute,”
Chaoqun Gong, Yuqin Dai, Ronghui Li, Achun Bao, Jun Li, Jian Yang, Yachao Zhang, and Xiu Li, · 2024
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“Mmm: Generative masked motion model,”
Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, and Chen Chen, · 2024
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“Visual autoregressive modeling: Scalable image generation via next-scale prediction,”
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang, · 2024
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“Motiongpt: Human motion as a foreign language,”
Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen, · 2024
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“Bamm: Bidirectional autoregressive motion model,”
Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee, Srijan Das, and Chen Chen, · 2024
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“Action2motion: Conditioned generation of 3d human motions,”
Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, and Li Cheng, · 2029
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