Fetching the paper…
Reading the bibliography…
Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
Earlier work this paper cites.
Cmu graphics lab motion capture database
Lab, C. G. 2000 · 2000
Earlier work this paper cites.
The KIT whole-body human motion database
Mandery, C.; Terlemez, Ö.; Do, M.; Vahrenkamp, N.; and Asfour, T. 2015 · 2015
Earlier work this paper cites.
The KIT motion-language dataset
Plappert, M.; Mandery, C.; and Asfour, T. 2016 · 2016
Earlier work this paper cites.
A recurrent variational autoencoder for human motion synthesis
Habibie, I.; Holden, D.; Schwarz, J.; Yearsley, J.; and Komura, T. 2017 · 2017
Earlier work this paper cites.
Auto-conditioned recurrent networks for extended complex human motion synthesis
Li, Z.; Zhou, Y.; Xiao, S.; He, C.; Huang, Z.; and Li, H. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
Earlier work this paper cites.
On human motion prediction using recurrent neural networks
Martinez, J.; Black, M. J.; and Romero, J. 2017 · 2017
Earlier work this paper cites.
Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
Earlier work this paper cites.
Hp-gan: Probabilistic 3d human motion prediction via gan
Barsoum, E.; Kender, J.; and Liu, Z. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
Earlier work this paper cites.
AMASS: Archive of motion capture as surface shapes
Mahmood, N.; Ghorbani, N.; Troje, N. F.; Pons-Moll, G.; and Black, M. J. 2019 · 2019
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Earlier work this paper cites.
On the effectiveness of adapter-based tuning for pretrained language model adaptation
He, R.; Liu, L.; Ye, H.; Tan, Q.; Ding, B.; Cheng, L.; Low, J.-W.; Bing, L.; and Si, L. 2021 · 2021
Cited alongside, same era.
TrajeVAE: Controllable Human Motion Generation from Trajectories
Kania, K.; Kowalski, M.; and Trzciński, T. 2021 · 2021
Cited alongside, same era.
Lightweight adapter tuning for multilingual speech translation
Le, H.; Pino, J.; Wang, C.; Gu, J.; Schwab, D.; and Besacier, L. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Ai choreographer: Music conditioned 3d dance generation with aist++
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E. L.; Ghasemipour, K.; Gontijo Lopes, R.; Karagol Ayan, B.; Salimans, T.; et al. 2022 · 2022
Later among the works it cites.
Motionclip: Exposing human motion generation to clip space
Tevet, G.; Gordon, B.; Hertz, A.; Bermano, A. H.; and Cohen-Or, D. 2022 · 2022
Later among the works it cites.
Scaling autoregressive models for content-rich text-to-image generation
Yu, J.; Xu, Y.; Koh, J. Y.; Luong, T.; Baid, G.; Wang, Z.; Vasudevan, V.; Ku, A.; Yang, Y.; Ayan, B. K.; et al. 2022 · 2022
Later among the works it cites.
Motiondiffuse: Text-driven human motion generation with diffusion model
Zhang, M.; Cai, Z.; Pan, L.; Hong, F.; Guo, X.; Yang, L.; and Liu, Z. 2022 · 2022
Later among the works it cites.
Music2dance: Dancenet for music-driven dance generation
Zhuang, W.; Wang, C.; Chai, J.; Wang, Y.; Shao, M.; and Xia, S. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, R.; Yang, S.; Ross, D. A.; and Kanazawa, A. 2021 · 2021
Cited alongside, same era.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Liu, X.; Ji, K.; Fu, Y.; Tam, W. L.; Du, Z.; Yang, Z.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Action-conditioned 3D human motion synthesis with transformer VAE
Petrovich, M.; Black, M. J.; and Varol, G. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J.; Bosma, M.; Zhao, V. Y.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2021 · 2021
Cited alongside, same era.
Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts
Guo, C.; Zuo, X.; Wang, S.; and Cheng, L. 2022b · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Vision-language models as success detectors
Du, Y.; Konyushkova, K.; Denil, M.; Raju, A.; Landon, J.; Hill, F.; de Freitas, N.; and Cabi, S. 2023 · 2023
Closest in time.
Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023
Closest in time.
Seeing is not always believing: A Quantitative Study on Human Perception of AI-Generated Images
Lu, Z.; Huang, D.; Bai, L.; Liu, X.; Qu, J.; and Ouyang, W. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Human Motion Diffusion Model
Tevet, G.; Raab, S.; Gordon, B.; Shafir, Y.; Cohen-or, D.; and Bermano, A. H. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
Closest in time.
Executing your Commands via Motion Diffusion in Latent Space
Xin, C.; Jiang, B.; Liu, W.; Huang, Z.; Fu, B.; Chen, T.; Yu, J.; and Yu, G. 2023 · 2023
Closest in time.
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Ye, Q.; Xu, H.; Xu, G.; Ye, J.; Yan, M.; Zhou, Y.; Wang, J.; Hu, A.; Shi, P.; Shi, Y.; Jiang, C.; Li, C.; Xu, Y.; Chen, H.; Tian, J.; Qi, Q.; Zhang, J.; and Huang, F. 2023 · 2023
Closest in time.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D.; Chen, J.; Shen, X.; Li, X.; and Elhoseiny, M. 2023 · 2023
Closest in time.
Action2motion: Conditioned generation of 3d human motions
Guo, C.; Zuo, X.; Wang, S.; Zou, S.; Sun, Q.; Deng, A.; Gong, M.; and Cheng, L. 2020 · 2029
Closest in time.