Fetching the paper…
Reading the bibliography…
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors.
Stacked quantizers for compositional vector compression
Julieta Martinez, Holger H. Hoos, and James J. Little · 2014
Earlier work this paper cites.
SMPL: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black · 2015
Earlier work this paper cites.
A deep learning framework for character motion synthesis and editing
Daniel Holden, Jun Saito, and Taku Komura · 2016
Earlier work this paper cites.
The KIT motion-language dataset
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2016
Earlier work this paper cites.
Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
Earlier work this paper cites.
The challenge of realistic music generation: modelling raw audio at scale
Sander Dieleman, Aäron van den Oord, and Karen Simonyan · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep video generation, prediction and completion of human action sequences
Haoye Cai, Chunyan Bai, Yu-Wing Tai, and Chi-Keung Tang · 2018
Earlier work this paper cites.
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2018
Earlier work this paper cites.
Language2pose: Natural language grounded pose forecasting
Chaitanya Ahuja and Louis-Philippe Morency · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Translating math formula images to latex sequences using deep neural networks with sequence-level training, 2019
Zelun Wang and Jyh-Charn Liu · 2019
Earlier work this paper cites.
Hierarchical quantized autoencoders
Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, and John Hughes · 2020
Earlier work this paper cites.
Perpetual motion: Generating unbounded human motion
Yan Zhang, Michael J. Black, and Siyu Tang · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Synthesis of compositional animations from textual descriptions
Anindita Ghosh, Noshaba Cheema, Cennet Oğuz, Christian Theobalt, and Philipp Slusallek · 2021
Earlier work this paper cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Earlier work this paper cites.
Text2gestures: A transformer-based network for generating emotive body gestures for virtual agents
Uttaran Bhattacharya, Nicholas Rewkowski, Abhishek Banerjee, Pooja Guhan, Aniket Bera, and Dinesh Manocha · 2021
Earlier work this paper cites.
Action-conditioned 3d human motion synthesis with transformer VAE
Mathis Petrovich, Michael J. Black, and Gül Varol · 2021
Cited alongside, same era.
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, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Temos: Generating diverse human motions from textual descriptions
Mathis Petrovich, Michael J Black, and Gül Varol · 2022
Cited alongside, same era.
Generating diverse and natural 3d human motions from text
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng · 2022
Cited alongside, same era.
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
Cited alongside, same era.
MMM: generative masked motion model
Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, and Chen Chen · 2023
Later among the works it cites.
Humantomato: Text-aligned whole-body motion generation
Shunlin Lu, Ling-Hao Chen, Ailing Zeng, Jing Lin, Ruimao Zhang, Lei Zhang, and Heung-Yeung Shum · 2023
Later among the works it cites.
Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training
Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, and Chang Wen Chen · 2023
Later among the works it cites.
FLAME: free-form language-based motion synthesis & editing
Jihoon Kim, Jiseob Kim, and Sungjoon Choi · 2023
Later among the works it cites.
Physdiff: Physics-guided human motion diffusion model
Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
TM2T: stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts
Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng · 2022
Cited alongside, same era.
TEACH: temporal action composition for 3d humans
Nikos Athanasiou, Mathis Petrovich, Michael J. Black, and Gül Varol · 2022
Cited alongside, same era.
Motionclip: Exposing human motion generation to CLIP space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H. Bermano, and Daniel Cohen-Or · 2022
Cited alongside, same era.
Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman · 2022
Cited alongside, same era.
Infogcn: Representation learning for human skeleton-based action recognition
Hyung-Gun Chi, Myoung Hoon Ha, Seung-geun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani · 2022
Cited alongside, same era.
Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Fg-t2m: Fine-grained text-driven human motion generation via diffusion model
Yin Wang, Zhiying Leng, Frederick W. B. Li, Shun-Cheng Wu, and Xiaohui Liang · 2023
Later among the works it cites.
Finemogen: Fine-grained spatio-temporal motion generation and editing
Mingyuan Zhang, Huirong Li, Zhongang Cai, Jiawei Ren, Lei Yang, and Ziwei Liu · 2023
Later among the works it cites.
Guided motion diffusion for controllable human motion synthesis
Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, and Siyu Tang · 2023
Later among the works it cites.
Diversemotion: Towards diverse human motion generation via discrete diffusion
Yunhong Lou, Linchao Zhu, Yaxiong Wang, Xiaohan Wang, and Yi Yang · 2023
Later among the works it cites.
Avatargpt: All-in-one framework for motion understanding, planning, generation and beyond
Zixiang Zhou, Yu Wan, and Baoyuan Wang · 2023
Later among the works it cites.
Human pose as compositional tokens
Zigang Geng, Chunyu Wang, Yixuan Wei, Ze Liu, Houqiang Li, and Han Hu · 2023
Later among the works it cites.
VQ-HPS: human pose and shape estimation in a vector-quantized latent space
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Antonio Agudo, and Francesc Moreno-Noguer · 2023
Later among the works it cites.
Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, and Dilip Krishnan · 2023
Later among the works it cites.
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
Later among the works it cites.
Motiongpt: Human motion synthesis with improved diversity and realism via GPT-3 prompting
José Ribeiro-Gomes, Tianhui Cai, Zoltán Ádám Milacski, Chen Wu, Aayush Prakash, Shingo Jason Takagi, Amaury Aubel, Daeil Kim, Alexandre Bernardino, and Fernando De la Torre · 2024
Closest in time.
Motiongpt: Finetuned llms are general-purpose motion generators
Yaqi Zhang, Di Huang, Bin Liu, Shixiang Tang, Yan Lu, Lu Chen, Lei Bai, Qi Chu, Nenghai Yu, and Wanli Ouyang · 2024
Closest in time.
Momask: Generative masked modeling of 3d human motions
Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng · 2024
Closest in time.
Zeyu Zhang, Akide Liu, Ian D. Reid, Richard Hartley, Bohan Zhuang, and Hao Tang · 2024
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 · 2024
Closest in time.