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Since 2023, Vector Quantization (VQ)-based discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion-based continuous generation methods in standard performance metrics.
Momask: Generative masked modeling of 3d human motions
Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng · 1910
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Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Auto-encoding variational bayes
Diederik P Kingma · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The kit motion-language dataset
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2016
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Carnegie mellon university - cmu graphics lab - motion capture library
Carnegie Mellon University CMU Graphics Lab motion capture library · 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
A Vaswani · 2017
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Generating animated videos of human activities from natural language descriptions
Angela S Lin, Lemeng Wu, Rodolfo Corona, Kevin Tai, Qixing Huang, and Raymond J Mooney · 2018
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Language2pose: Natural language grounded pose forecasting
Chaitanya Ahuja and Louis-Philippe Morency · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Amass: Archive of motion capture as surface shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black · 2019
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Language models are few-shot learners
Tom B Brown · 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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Dance2music: Automatic dance-driven music generation
Gunjan Aggarwal and Devi Parikh · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Temos: Generating diverse human motions from textual descriptions
Mathis Petrovich, Michael J Black, and Gül Varol · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 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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Towards diverse and natural scene-aware 3d human motion synthesis
Jingbo Wang, Yu Rong, Jingyuan Liu, Sijie Yan, Dahua Lin, and Bo Dai · 2022
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Motiondiffuse: Text-driven human motion generation with diffusion model
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu · 2022
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Make-an-animation: Large-scale text-conditional 3d human motion generation
Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh, and Sonal Gupta · 2023
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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
Cited alongside, same era.
Remos: Reactive 3d motion synthesis for two-person interactions
Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, and Philipp Slusallek · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Cited alongside, same era.
Motion flow matching for human motion synthesis and editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen, Basura Fernando, Yuki M Asano, Efstratios Gavves, Pascal Mettes, Bjorn Ommer, and Cees GM Snoek · 2023
Cited alongside, same era.
Diffusion-based generation, optimization, and planning in 3d scenes
Siyuan Huang, Zan Wang, Puhao Li, Baoxiong Jia, Tengyu Liu, Yixin Zhu, Wei Liang, and Song-Chun Zhu · 2023
Mogo: Rq hierarchical causal transformer for high-quality 3d human motion generation
Dongjie Fu · 2024
Closest in time.
Diffusion implicit policy for unpaired scene-aware motion synthesis
Jingyu Gong, Chong Zhang, Fengqi Liu, Ke Fan, Qianyu Zhou, Xin Tan, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma · 2024
Closest in time.
Dart: Denoising autoregressive transformer for scalable text-to-image generation
Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai · 2024
Closest in time.
Intermask: 3d human interaction generation via collaborative masked modelling
Muhammad Gohar Javed, Chuan Guo, Li Cheng, and Xingyu Li · 2024
Closest in time.
Scaling up dynamic human-scene interaction modeling
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Cited alongside, same era.
Motiongpt: Human motion as a foreign language
Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen · 2023
Cited alongside, same era.
Gmd: Controllable human motion synthesis via guided diffusion models
Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, and Siyu Tang · 2023
Cited alongside, same era.
Flame: Free-form language-based motion synthesis & editing
Jihoon Kim, Jiseob Kim, and Sungjoon Choi · 2023
Cited alongside, same era.
Object motion guided human motion synthesis
Jiaman Li, Jiajun Wu, and C Karen Liu · 2023
Cited alongside, same era.
Motion-x: A large-scale 3d expressive whole-body human motion dataset
Jing Lin, Ailing Zeng, Shunlin Lu, Yuanhao Cai, Ruimao Zhang, Haoqian Wang, and Lei Zhang · 2023
Cited alongside, same era.
Diversemotion: Towards diverse human motion generation via discrete diffusion
Yunhong Lou, Linchao Zhu, Yaxiong Wang, Xiaohan Wang, and Yi Yang · 2023
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Cited alongside, same era.
Nan Jiang, Zhiyuan Zhang, Hongjie Li, Xiaoxuan Ma, Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, and Siyuan Huang · 2024
Closest in time.
Act as you wish: Fine-grained control of motion diffusion model with hierarchical semantic graphs
Peng Jin, Yang Wu, Yanbo Fan, Zhongqian Sun, Wei Yang, and Li Yuan · 2024
Closest in time.
Mas: Multi-view ancestral sampling for 3d motion generation using 2d diffusion
Roy Kapon, Guy Tevet, Daniel Cohen-Or, and Amit H Bermano · 2024
Closest in time.
Optimizing diffusion noise can serve as universal motion priors
Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, and Siyu Tang · 2024
Closest in time.
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 · 2024
Closest in time.
Scamo: Exploring the scaling law in autoregressive motion generation model
Shunlin Lu, Jingbo Wang, Zeyu Lu, Ling-Hao Chen, Wenxun Dai, Junting Dong, Zhiyang Dou, Bo Dai, and Ruimao Zhang · 2024
Closest in time.
Multi-track timeline control for text-driven 3d human motion generation
Mathis Petrovich, Or Litany, Umar Iqbal, Michael J Black, Gul Varol, Xue Bin Peng, and Davis Rempe · 2024
Closest in time.
Motion-2-to-3: Leveraging 2d motion data to boost 3d motion generation
Huaijin Pi, Ruoxi Guo, Zehong Shen, Qing Shuai, Zechen Hu, Zhumei Wang, Yajiao Dong, Ruizhen Hu, Taku Komura, Sida Peng, et al · 2024
Closest in time.
Interactive character control with auto-regressive motion diffusion models
Yi Shi, Jingbo Wang, Xuekun Jiang, Bingkun Lin, Bo Dai, and Xue Bin Peng · 2024
Closest in time.
Closd: Closing the loop between simulation and diffusion for multi-task character control
Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng, Amit H Bermano, and Michiel van de Panne · 2024
Closest in time.
Thor: Text to human-object interaction diffusion via relation intervention
Qianyang Wu, Ye Shi, Xiaoshui Huang, Jingyi Yu, Lan Xu, and Jingya Wang · 2024
Closest in time.
Omnicontrol: Control any joint at any time for human motion generation
Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, and Huaizu Jiang · 2024
Closest in time.
Mogents: Motion generation based on spatial-temporal joint modeling
Weihao Yuan, Weichao Shen, Yisheng He, Yuan Dong, Xiaodong Gu, Zilong Dong, Liefeng Bo, and Qixing Huang · 2024
Closest in time.
Tedi: Temporally-entangled diffusion for long-term motion synthesis
Zihan Zhang, Richard Liu, Rana Hanocka, and Kfir Aberman · 2024
Closest in time.
Infinidreamer: Arbitrarily long human motion generation via segment score distillation
Wenjie Zhuo, Fan Ma, and Hehe Fan · 2024
Closest in time.
Ready-to-react: Online reaction policy for two-character interaction generation
Zhi Cen, Huaijin Pi, Sida Peng, Qing Shuai, Yujun Shen, Hujun Bao, Xiaowei Zhou, and Ruizhen Hu · 2025
Closest in time.
Como: Controllable motion generation through language guided pose code editing
Yiming Huang, Weilin Wan, Yue Yang, Chris Callison-Burch, Mark Yatskar, and Lingjie Liu · 2025
Closest in time.
Controllable human-object interaction synthesis
Jiaman Li, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, and C Karen Liu · 2025
Closest in time.
Revisit human-scene interaction via space occupancy
Xinpeng Liu, Haowen Hou, Yanchao Yang, Yong-Lu Li, and Cewu Lu · 2025
Closest in time.
Humos: Human motion model conditioned on body shape
Shashank Tripathi, Omid Taheri, Christoph Lassner, Michael Black, Daniel Holden, and Carsten Stoll · 2025
Closest in time.
Givt: Generative infinite-vocabulary transformers
Michael Tschannen, Cian Eastwood, and Fabian Mentzer · 2025
Closest in time.
Generating human interaction motions in scenes with text control
Hongwei Yi, Justus Thies, Michael J Black, Xue Bin Peng, and Davis Rempe · 2025
Closest in time.
Motion mamba: Efficient and long sequence motion generation
Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang · 2025
Closest in time.
Smoodi: Stylized motion diffusion model
Lei Zhong, Yiming Xie, Varun Jampani, Deqing Sun, and Huaizu Jiang · 2025
Closest in time.
Emdm: Efficient motion diffusion model for fast and high-quality motion generation
Wenyang Zhou, Zhiyang Dou, Zeyu Cao, Zhouyingcheng Liao, Jingbo Wang, Wenjia Wang, Yuan Liu, Taku Komura, Wenping Wang, and Lingjie Liu · 2025
Closest in time.
Parco: Part-coordinating text-to-motion synthesis
Qiran Zou, Shangyuan Yuan, Shian Du, Yu Wang, Chang Liu, Yi Xu, Jie Chen, and Xiangyang Ji · 2025
Closest in time.