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Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020) · 2010
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Realtime style transfer for unlabeled heterogeneous human motion
Xia, S., Wang, C., Chai, J., and Hodgins, J. (2015) · 2015
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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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Phase-functioned neural networks for character control
Holden, D., Komura, T., and Saito, J. (2017) · 2017
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On human motion prediction using recurrent neural networks
Martinez, J., Black, M. J., and Romero, J. (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
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Unpaired motion style transfer from video to animation
Aberman, K., Weng, Y., Lischinski, D., Cohen-Or, D., and Chen, B. (2020) · 2020
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Adult2child: Motion style transfer using cyclegans
Dong, Y., Aristidou, A., Shamir, A., Mahler, M., and Jain, E. (2020) · 2020
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Moglow: Probabilistic and controllable motion synthesis using normalising flows
Henter, G. E., Alexanderson, S., and Beskow, J. (2020) · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Character controllers using motion vaes
Ling, H. Y., Zinno, F., Cheng, G., and Van De Panne, M. (2020) · 2020
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Bond-Taylor, S., Leach, A., Long, Y., and Willcocks, C. G. (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J. (2021) · 2021
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Xiao, Z., Kreis, K., and Vahdat, A. (2021) · 2021
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Human motion modeling with deep learning: A survey
Ye, Z., Wu, H., and Jia, J. (2021) · 2021
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Denoising diffusion probabilistic models for styled walking synthesis
Findlay, E. J., Zhang, H., Chang, Z., and Shum, H. P. (2022) · 2022
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Kawar, B., Elad, M., Ermon, S., and Song, J. (2022) · 2022
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Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M. (2021) · 2021
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Nichol, A. Q. and Dhariwal, P. (2021) · 2021
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