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Text-to-motion generation is a formidable task, aiming to produce human motions that align with the input text while also adhering to human capabilities and physical laws.
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Diederik P Kingma and Max Welling · 2013
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Diederik P Kingma and Jimmy Ba · 2014
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The kit whole-body human motion database
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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The kit motion-language dataset
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Mocogan: Decomposing motion and content for video generation
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Language2pose: Natural language grounded pose forecasting
Chaitanya Ahuja and Louis-Philippe Morency · 2019
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Dancing to music
Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, and Jan Kautz · 2019
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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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 · 2020
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Text2gestures: A transformer-based network for generating emotive body gestures for virtual agents
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Mathis Petrovich, Michael J Black, and Gül Varol · 2022
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Shunted Self-Attention via Multi-Scale Token Aggregation
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Motionclip: Exposing human motion generation to clip space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or · 2022
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Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano · 2022
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