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Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.
Retargetting motion to new characters
Michael Gleicher · 1998
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A hierarchical approach to interactive motion editing for human-like figures
Jehee Lee and Sung Yong Shin · 1999
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A survey of computer vision-based human motion capture
Thomas B. Moeslund and Erik Granum · 2001
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Animating non-humanoid characters with human motion data
Katsu Yamane, Yuka Ariki, and Jessica Hodgins · 2010
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Creature features: Online motion puppetry for non-human characters
Yeongho Seol, Carol O’Sullivan, and Jehee Lee · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Recurrent network models for human dynamics
K. Fragkiadaki, S. Levine, P. Felsen, and J. Malik · 2015
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The kit motion-language dataset
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2016
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Motion style retargeting to characters with different morphologies
Michel Abdul-Massih, Innfarn Yoo, and Bedrich Benes · 2017
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Hp-gan: Probabilistic 3d human motion prediction via gan, 2017
Emad Barsoum, John Kender, and Zicheng Liu · 2017
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Deep representation learning for human motion prediction and classification
J. Butepage, M. J. Black, D. Kragic, and H. Kjellstrom · 2017
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On human motion prediction using recurrent neural networks
Julieta Martinez, Michael J. Black, and Javier Romero · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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3d menagerie: Modeling the 3d shape and pose of animals
Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black · 2017
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Recurrent transition networks for character locomotion
Félix G. Harvey and Christopher Pal · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Lions and tigers and bears: Capturing non-rigid, 3d, articulated shape from images
Silvia Zuffi, Angjoo Kanazawa, and Michael J. Black · 2018
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Creatures great and smal: Recovering the shape and motion of animals from video
Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon, and Roberto Cipolla · 2019
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Human motion prediction via spatio-temporal inpainting
A. Hernandez, J. Gall, and F. Moreno · 2019
Cited alongside, same era.
Dancing to music
Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, and Jan Kautz · 2019
Cited alongside, same era.
AMASS: Archive of motion capture as surface shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, and Michael J. Black · 2019
Cited alongside, same era.
Learning diverse stochastic human-action generators by learning smooth latent transitions
Zhenyi Wang, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, and Changyou Chen · 2019
Cited alongside, same era.
On the continuity of rotation representations in neural networks
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li · 2019
Cited alongside, same era.
Skeleton-aware networks for deep motion retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, and Baoquan Chen · 2020
Real-time controllable motion transition for characters
Xiangjun Tang, He Wang, Bo Hu, Xu Gong, Ruifan Yi, Qilong Kou, and Xiaogang Jin · 2022
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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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Humanise: Language-conditioned human motion generation in 3d scenes
Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Wei Liang, and Siyuan Huang · 2022
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Banmo: Building animatable 3d neural models from many casual videos
Gengshan Yang, Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, and Hanbyul Joo · 2022
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Lassie: Learning articulated shapes from sparse image ensemble via 3d part discovery
Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, and Varun Jampani · 2022
Later among the works it cites.
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Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Robust motion in-betweening
Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai, and Christopher Pal · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Articulation-aware canonical surface mapping
Nilesh Kulkarni, Abhinav Gupta, David F Fouhey, and Shubham Tulsiani · 2020
Cited alongside, same era.
History repeats itself: Human motion prediction via motion attention
Mao Wei, Liu Miaomiao, and Salzemann Mathieu · 2020
Cited alongside, same era.
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu · 2022
Later among the works it cites.
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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Dance revolution: Long-term dance generation with music via curriculum learning, 2023
Ruozi Huang, Huang Hu, Wei Wu, Kei Sawada, Mi Zhang, and Daxin Jiang · 2023
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Motiongpt: Human motion as a foreign language
Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen · 2023
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Nifty: Neural object interaction fields for guided human motion synthesis
Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, and Leonidas Guibas · 2023
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Motion-x: A large-scale 3d expressive whole-body human motion dataset, 2023
Jing Lin, Ailing Zeng, Shunlin Lu, Yuanhao Cai, Ruimao Zhang, Haoqian Wang, and Lei Zhang · 2023
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Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2023
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Transfer4d: A framework for frugal motion capture and deformation transfer
Shubh Maheshwari, Rahul Narain, and Ramya Hebbalaguppe · 2023
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Human motion diffusion model
Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano · 2023
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Edge: Editable dance generation from music
Jonathan Tseng, Rodrigo Castellon, and Karen Liu · 2023
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Hmc: Hierarchical mesh coarsening for skeleton-free motion retargeting
Haoyu Wang, Shaoli Huang, Fang Zhao, Chun Yuan, and Ying Shan · 2023
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Animal3d: A comprehensive dataset of 3d animal pose and shape
Jiacong Xu, Yi Zhang, Jiawei Peng, Wufei Ma, Artur Jesslen, Pengliang Ji, Qixin Hu, Jiehua Zhang, Qihao Liu, Jiahao Wang, et al · 2023
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Hi-lassie: High-fidelity articulated shape and skeleton discovery from sparse image ensemble
Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, and Varun Jampani · 2023
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PhysDiff: Physics-guided human motion diffusion model
Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz · 2023
Closest in time.