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Synthesizing interaction-involved human motions has been challenging due to the high complexity of 3D environments and the diversity of possible human behaviors within.
Interactive control of avatars animated with human motion data
Jehee Lee, Jinxiang Chai, Paul SA Reitsma, Jessica K Hodgins, and Nancy S Pollard · 2002
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Motion graphs
Kovar Lucas, Gleicher Michael, and Pighin Frédéric · 2002
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Precomputing avatar behavior from human motion data
Jehee Lee and Kang Hoon Lee · 2004
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Kang Hoon Lee, Myung Geol Choi, and Jehee Lee · 2006
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Near-optimal character animation with continuous control
Adrien Treuille, Yongjoon Lee, and Zoran Popović · 2007
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Interaction patches for multi-character animation
Hubert PH Shum, Taku Komura, Masashi Shiraishi, and Shuntaro Yamazaki · 2008
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Factored conditional restricted boltzmann machines for modeling motion style
Graham W Taylor and Geoffrey E Hinton · 2009
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Continuous character control with low-dimensional embeddings
Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun · 2012
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Shape2pose: Human-centric shape analysis
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Adam: A method for stochastic optimization
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Simon Clavet · 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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Motion grammars for character animation
Kyunglyul Hyun, Kyungho Lee, and Jehee Lee · 2016
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Pigraphs: learning interaction snapshots from observations
Manolis Savva, Angel X Chang, Pat Hanrahan, Matthew Fisher, and Matthias Nießner · 2016
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Phase-functioned neural networks for character control
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Julieta Martinez, Michael J Black, and Javier Romero · 2017
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https://www.mixamo.com , 2017
Adobe’s Mixamo · 2017
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Jeongseok Lee, Michael X Grey, Sehoon Ha, Tobias Kunz, Sumit Jain, Yuting Ye, Siddhartha S Srinivasa, Mike Stilman, and C Karen Liu · 2018
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Interactive character animation by learning multi-objective control
Kyungho Lee, Seyoung Lee, and Jehee Lee · 2018
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, 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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Model predictive control with a visuomotor system for physics-based character animation
Haegwang Eom, Daseong Han, Joseph S Shin, and Junyong Noh · 2019
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Resolving 3D human pose ambiguities with 3D scene constraints
Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas, and Michael J. Black · 2019
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Scalable muscle-actuated human simulation and control
Seunghwan Lee, Moonseok Park, Kyoungmin Lee, and Jehee Lee · 2019
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Learning predict-and-simulate policies from unorganized human motion data
Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee · 2019
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Pytorch: An imperative style, high-performance deep learning library
Learning time-critical responses for interactive character control
Kyungho Lee, Sehee Min, Sunmin Lee, and Jehee Lee · 2021
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Learning a family of motor skills from a single motion clip
Seyoung Lee, Sunmin Lee, Yongwoo Lee, and Jehee Lee · 2021
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Ai choreographer: Music conditioned 3d dance generation with aist++
Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa · 2021
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Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Action-conditioned 3d human motion synthesis with transformer vae
Mathis Petrovich, Michael J Black, and Gül Varol · 2021
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Synthesizing long-term 3d human motion and interaction in 3d scenes
Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, and Xiaolong Wang · 2021
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