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This work targets to construct a robust human pose prior.
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Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
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Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it smpl: Automatic estimation of 3d human pose and shape from a single image. In: ECCV (2016)
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Liu, Q., Wang, D.: Stein variational gradient descent: A general purpose bayesian inference algorithm. NeurIPS (2016)
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Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: CVPR (2018)
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Von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3d human pose in the wild using imus and a moving camera. In: ECCV (2018)
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Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: Amass: Archive of motion capture as surface shapes. In: ICCV (2019)
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Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A., Tzionas, D., Black, M.J.: Expressive body capture: 3d hands, face, and body from a single image. In: CVPR (2019)
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Särkkä, S., Solin, A.: Applied stochastic differential equations, vol. 10. Cambridge University Press (2019)
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Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. NeurIPS (2019)
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Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: CVPR (2019)
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Georgakis, G., Li, R., Karanam, S., Chen, T., Košecká, J., Wu, Z.: Hierarchical kinematic human mesh recovery. In: ECCV (2020)
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM (2020)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)
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Ling, H.Y., Zinno, F., Cheng, G., Van De Panne, M.: Character controllers using motion vaes. TOG (2020)
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. NeurIPS (2021)
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Guzov, V., Mir, A., Sattler, T., Pons-Moll, G.: Human poseitioning system (hps): 3d human pose estimation and self-localization in large scenes from body-mounted sensors. In: CVPR (2021)
Xu, Y., Zhang, J., Zhang, Q., Tao, D.: ViTPose: Simple vision transformer baselines for human pose estimation. In: Advances in Neural Information Processing Systems (2022)
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Cho, H., Kim, J.: Generative approach for probabilistic human mesh recovery using diffusion models. In: ICCV (2023)
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Chung, H., Kim, J., Kim, S., Ye, J.C.: Parallel diffusion models of operator and image for blind inverse problems. In: CVPR (2023)
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Ci, H., Wu, M., Zhu, W., Ma, X., Dong, H., Zhong, F., Wang, Y.: Gfpose: Learning 3d human pose prior with gradient fields. In: CVPR (2023)
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2021
Cited alongside, same era.
2021
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Rempe, D., Birdal, T., Hertzmann, A., Yang, J., Sridhar, S., Guibas, L.J.: Humor: 3d human motion model for robust pose estimation. In: ICCV (2021)
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Song, Y., Durkan, C., Murray, I., Ermon, S.: Maximum likelihood training of score-based diffusion models. NeurIPS (2021)
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Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., Yoon, S.: Perception prioritized training of diffusion models. In: CVPR (2022)
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Chung, H., Sim, B., Ryu, D., Ye, J.C.: Improving diffusion models for inverse problems using manifold constraints. NeurIPS (2022)
2022
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Davydov, A., Remizova, A., Constantin, V., Honari, S., Salzmann, M., Fua, P.: Adversarial parametric pose prior. In: CVPR (2022)
2022
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2023
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Holmquist, K., Wandt, B.: Diffpose: Multi-hypothesis human pose estimation using diffusion models. In: ICCV (2023)
2023
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Lin, J., Zeng, A., Wang, H., Zhang, L., Li, Y.: One-stage 3d whole-body mesh recovery with component aware transformer. In: CVPR (2023)
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Wang, H., Du, X., Li, J., Yeh, R.A., Shakhnarovich, G.: Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In: CVPR (2023)
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