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Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries.
Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2010
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Recurrent network models for human dynamics
Fragkiadaki, K.; Levine, S.; Felsen, P.; and Malik, J. 2015 · 2015
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SMPL: A Skinned Multi-Person Linear Model
Loper, M.; Mahmood, N.; Romero, J.; Pons-Moll, G.; and Black, M. J. 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
Earlier work this paper cites.
The KIT motion-language dataset
Plappert, M.; Mandery, C.; and Asfour, T. 2016 · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
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Text2action: Generative adversarial synthesis from language to action
Ahn, H.; Ha, T.; Choi, Y.; Yoo, H.; and Oh, S. 2018 · 2018
Earlier work this paper cites.
Generating Animated Videos of Human Activities from Natural Language Descriptions
Lin, A. S.; Wu, L.; Corona, R.; Tai, K.; Huang, Q.; and Mooney, R. J. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
Cited alongside, same era.
Language2pose: Natural language grounded pose forecasting
Ahuja, C.; and Morency, L.-P. 2019 · 2019
Cited alongside, same era.
Human motion prediction via learning local structure representations and temporal dependencies
Guo, X.; and Choi, J. 2019 · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Cited alongside, same era.
AMASS: Archive of Motion Capture as Surface Shapes
Mahmood, N.; Ghorbani, N.; Troje, N. F.; Pons-Moll, G.; and Black, M. J. 2019 · 2019
Cited alongside, same era.
On the continuity of rotation representations in neural networks
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A.; Dhariwal, P.; Ramesh, A.; Shyam, P.; Mishkin, P.; McGrew, B.; Sutskever, I.; and Chen, M. 2021 · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
Later among the works it cites.
Action-conditioned 3d human motion synthesis with transformer vae
Petrovich, M.; Black, M. J.; and Varol, G. 2021 · 2021
Later among the works it cites.
BABEL: Bodies, Action and Behavior with English Labels
Punnakkal, A. R.; Chandrasekaran, A.; Athanasiou, N.; Quiros-Ramirez, A.; and Black, M. J. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
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Zhou, Y.; Barnes, C.; Lu, J.; Yang, J.; and Li, H. 2019 · 2019
Cited alongside, same era.
Robust motion in-betweening
Harvey, F. G.; Yurick, M.; Nowrouzezahrai, D.; and Pal, C. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Karras, T.; Laine, S.; Aittala, M.; Hellsten, J.; Lehtinen, J.; and Aila, T. 2020 · 2020
Cited alongside, same era.
Banitalebi-Dehkordi, A.; and Zhang, Y. 2021 · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
Cited alongside, same era.
Synthesis of compositional animations from textual descriptions
Ghosh, A.; Cheema, N.; Oguz, C.; Theobalt, C.; and Slusallek, P. 2021 · 2021
Cited alongside, same era.
A Unified Framework for Real Time Motion Completion
Duan, Y.; Lin, Y.; Zou, Z.; Yuan, Y.; Qian, Z.; and Zhang, B. 2022 · 2022
Closest in time.
Generating Diverse and Natural 3D Human Motions From Text
Guo, C.; Zou, S.; Zuo, X.; Wang, S.; Ji, W.; Li, X.; and Cheng, L. 2022 · 2022
Closest in time.
TEMOS: Generating diverse human motions from textual descriptions
Petrovich, M.; Black, M. J.; and Varol, G. 2022 · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
Closest in time.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E.; Ghasemipour, S. K. S.; Ayan, B. K.; Mahdavi, S. S.; Lopes, R. G.; et al. 2022 · 2022
Closest in time.
Stylegan-xl: Scaling stylegan to large diverse datasets
Sauer, A.; Schwarz, K.; and Geiger, A. 2022 · 2022
Closest in time.
ActFormer: A GAN Transformer Framework towards General Action-Conditioned 3D Human Motion Generation
Song, Z.; Wang, D.; Jiang, N.; Fang, Z.; Ding, C.; Gan, W.; and Wu, W. 2022 · 2022
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.