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The creation of plausible and controllable 3D human motion animations is a long-standing problem that requires a manual intervention of skilled artists.
Visual perception of biological motion and a model for its analysis
Gunnar Johansson · 1973
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Discrete cosine transform
Nasir Ahmed, T. Natarajan, and Kamisetty R Rao · 1974
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Verbs and adverbs: Multidimensional motion interpolation
Charles Rose, Michael F Cohen, and Bobby Bodenheimer · 1998
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Image inpainting
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester · 2000
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Artist-directed inverse-kinematics using radial basis function interpolation
Charles F Rose III, Peter-Pike J Sloan, and Michael F Cohen · 2001
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Modeling human motion using binary latent variables
Graham W Taylor, Geoffrey E Hinton, and Sam T Roweis · 2007
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Recurrent network models for human dynamics
Katerina Fragkiadaki, Sergey Levine, Panna Felsen, and Jitendra Malik · 2015
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Learning motion manifolds with convolutional autoencoders
Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep unsupervised clustering with gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2016
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A deep learning framework for character motion synthesis and editing
Daniel Holden, Jun Saito, and Taku Komura · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Learning human motion models for long-term predictions
Partha Ghosh, Jie Song, Emre Aksan, and Otmar Hilliges · 2017
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Deligan: Generative adversarial networks for diverse and limited data
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R Venkatesh Babu · 2017
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Phase-functioned neural networks for character control
Daniel Holden, Taku Komura, and Jun Saito · 2017
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Auto-conditioned recurrent networks for extended complex human motion synthesis
Zimo Li, Yi Zhou, Shuangjiu Xiao, Chong He, Zeng Huang, and Hao Li · 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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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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The pose knows: Video forecasting by generating pose futures
Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert · 2017
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Hp-gan: Probabilistic 3d human motion prediction via gan
Emad Barsoum, John Kender, and Zicheng Liu · 2018
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Accurate and diverse sampling of sequences based on a “best of many” sample objective
Apratim Bhattacharyya, Bernt Schiele, and Mario Fritz · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Adversarial geometry-aware human motion prediction
Liang-Yan Gui, Yu-Xiong Wang, Xiaodan Liang, and José MF Moura · 2018
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Recurrent semi-supervised classification and constrained adversarial generation with motion capture data
Felix G Harvey, Julien Roy, David Kanaa, and Christopher Pal · 2018
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Convolutional sequence to sequence model for human dynamics
Chen Li, Zhen Zhang, Wee Sun Lee, and Gim Hee Lee · 2018
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Neural state machine for character-scene interactions
Sebastian Starke, He Zhang, Taku Komura, and Jun Saito · 2019
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T-cvae: Transformer-based conditioned variational autoencoder for story completion
Tianming Wang and Xiaojun Wan · 2019
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Combining recurrent neural networks and adversarial training for human motion synthesis and control
Zhiyong Wang, Jinxiang Chai, and Shihong Xia · 2019
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Foreground-aware image inpainting
Wei Xiong, Jiahui Yu, Zhe Lin, Jimei Yang, Xin Lu, Connelly Barnes, and Jiebo Luo · 2019
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Diverse trajectory forecasting with determinantal point processes
Ye Yuan and Kris Kitani · 2019
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Predicting 3d human dynamics from video
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Xiao Lin and Mohamed R Amer · 2018
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Image inpainting for irregular holes using partial convolutions
Guilin Liu, Fitsum A Reda, Kevin J Shih, Ting-Chun Wang, Andrew Tao, and Bryan Catanzaro · 2018
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Quaternet: A quaternion-based recurrent model for human motion
Dario Pavllo, David Grangier, and Michael Auli · 2018
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Long-term human motion prediction by modeling motion context and enhancing motion dynamic
Yongyi Tang, Lin Ma, Wei Liu, and Weishi Zheng · 2018
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Mt-vae: Learning motion transformations to generate multimodal human dynamics
Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, and Honglak Lee · 2018
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Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
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Mode-adaptive neural networks for quadruped motion control
He Zhang, Sebastian Starke, Taku Komura, and Jun Saito · 2018
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Jason Y Zhang, Panna Felsen, Angjoo Kanazawa, and Jitendra Malik · 2019
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Pluralistic image completion
Chuanxia Zheng, Tat-Jen Cham, and Jianfei Cai · 2019
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Attention, please: A spatio-temporal transformer for 3d human motion prediction
Emre Aksan, Peng Cao, Manuel Kaufmann, and Otmar Hilliges · 2020
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Piigan: Generative adversarial networks for pluralistic image inpainting
Weiwei Cai and Zhanguo Wei · 2020
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Learning progressive joint propagation for human motion prediction
Yujun Cai, Lin Huang, Yiwei Wang, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Xu Yang, Yiheng Zhu, Xiaohui Shen, et al · 2020
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Dynamic future net: Diversified human motion generation
Wenheng Chen, He Wang, Yi Yuan, Tianjia Shao, and Kun Zhou · 2020
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Learning dynamic relationships for 3d human motion prediction
Qiongjie Cui, Huaijiang Sun, and Fei Yang · 2020
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Monster mash: a single-view approach to casual 3d modeling and animation
Marek Dvorožňák, Daniel Sỳkora, Cassidy Curtis, Brian Curless, Olga Sorkine-Hornung, and David Salesin · 2020
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Probabilistic character motion synthesis using a hierarchical deep latent variable model
Saeed Ghorbani, Calden Wloka, Ali Etemad, Marcus A Brubaker, and Nikolaus F Troje · 2020
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Moglow: Probabilistic and controllable motion synthesis using normalising flows
Gustav Eje Henter, Simon Alexanderson, and Jonas Beskow · 2020
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Constructing human motion manifold with sequential networks
Deok-Kyeong Jang and Sung-Hee Lee · 2020
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Convolutional autoencoders for human motion infilling
Manuel Kaufmann, Emre Aksan, Jie Song, Fabrizio Pece, Remo Ziegler, and Otmar Hilliges · 2020
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Motion prediction using temporal inception module
Tim Lebailly, Sena Kiciroglu, Mathieu Salzmann, Pascal Fua, and Wei Wang · 2020
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Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction
Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, and Qi Tian · 2020
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History repeats itself: Human motion prediction via motion attention
Wei Mao, Miaomiao Liu, and Mathieu Salzmann · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Dlow: Diversifying latent flows for diverse human motion prediction
Ye Yuan and Kris Kitani · 2020
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We are more than our joints: Predicting how 3d bodies move
Yan Zhang, Michael J Black, and Siyu Tang · 2020
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A-nerf: Surface-free human 3d pose refinement via neural rendering
Shih-Yang Su, Frank Yu, Michael Zollhoefer, and Helge Rhodin · 2021
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Learning motion priors for 4d human body capture in 3d scenes
Siwei Zhang, Yan Zhang, Federica Bogo, Marc Pollefeys, and Siyu Tang · 2021
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