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Motion completion is a challenging and long-discussed problem, which is of great significance in film and game applications.
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Interactive generation of human animation with deformable motion models
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Jianyuan Min and Jinxiang Chai · 2012
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Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Efficient nonlinear markov models for human motion
Andreas M Lehrmann, Peter V Gehler, and Sebastian Nowozin · 2014
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Learning motion manifolds with convolutional autoencoders
Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Model-based adversarial imitation learning
Nir Baram, Oron Anschel, and Shie Mannor · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Taoran Tang, Hanyang Mao, and Jia Jia · 2018
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Data-driven autocompletion for keyframe animation
Xinyi Zhang and Michiel van de Panne · 2018
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Drecon: data-driven responsive control of physics-based characters
Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes · 2019
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Tangent-space optimization for interactive animation control
Loïc Ciccone, Cengiz Öztireli, and Robert W Sumner · 2019
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A neural temporal model for human motion prediction
Anand Gopalakrishnan, Ankur Mali, Dan Kifer, Lee Giles, and Alexander G Ororbia · 2019
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Human motion prediction via spatio-temporal inpainting
Alejandro Hernandez, Jurgen Gall, and Francesc Moreno-Noguer · 2019
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Pytorch: An imperative style, high-performance deep learning library
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Efficient transformers: A survey
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