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We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation.
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Zhao, R., Su, H., Ji, Q.: Bayesian adversarial human motion synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6225–6234 (2020)
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Peng, X.B., Ma, Z., Abbeel, P., Levine, S., Kanazawa, A.: Amp: Adversarial motion priors for stylized physics-based character control. ACM Trans. Graph. 40
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Petrovich, M., Black, M.J., Varol, G.: Action-conditioned 3D human motion synthesis with transformer VAE. In: International Conference on Computer Vision (ICCV) (2021)
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Cervantes, P., Sekikawa, Y., Sato, I., Shinoda, K.: Implicit neural representations for variable length human motion generation. In: European Conference on Computer Vision. pp. 356–372. Springer (2022)
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Li, B., Zhao, Y., Zhelun, S., Sheng, L.: Danceformer: Music conditioned 3d dance generation with parametric motion transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 1272–1279 (2022)
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Liao, Z., Yang, J., Saito, J., Pons-Moll, G., Zhou, Y.: Skeleton-free pose transfer for stylized 3d characters. In: European Conference on Computer Vision. pp. 640–656. Springer (2022)
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Petrovich, M., Black, M.J., Varol, G.: TEMOS: Generating diverse human motions from textual descriptions. In: European Conference on Computer Vision (ECCV) (2022)
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Petrovich, M., Black, M.J., Varol, G.: Temos: Generating diverse human motions from textual descriptions. In: European Conference on Computer Vision. pp. 480–497. Springer (2022)
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Müller, N., Siddiqui, Y., Porzi, L., Bulo, S.R., Kontschieder, P., Nießner, M.: Diffrf: Rendering-guided 3d radiance field diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4328–4338 (2023)
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