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We introduce the Cross Human Motion Diffusion Model (CrossDiff), a novel approach for generating high-quality human motion based on textual descriptions.
2012
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Plappert, M., Mandery, C., Asfour, T.: The kit motion-language dataset. Big data 4
2016
Earlier work this paper cites.
Ghosh, P., Song, J., Aksan, E., Hilliges, O.: Learning human motion models for long-term predictions. In: 2017 International Conference on 3D Vision (3DV). pp. 458–466. IEEE (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Ahuja, C., Morency, L.P.: Language2pose: Natural language grounded pose forecasting. In: 2019 International Conference on 3D Vision (3DV). pp. 719–728. IEEE (2019)
2019
Earlier work this paper cites.
Lee, H.Y., Yang, X., Liu, M.Y., Wang, T.C., Lu, Y.D., Yang, M.H., Kautz, J.: Dancing to music. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: Amass: Archive of motion capture as surface shapes. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 5442–5451 (2019)
2019
Earlier work this paper cites.
Guo, C., Zuo, X., Wang, S., Zou, S., Sun, Q., Deng, A., Gong, M., Cheng, L.: Action2motion: Conditioned generation of 3d human motions. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 2021–2029 (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 214–223 (2020)
2020
Earlier work this paper cites.
Song, Y., Ermon, S.: Improved techniques for training score-based generative models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Yoon, Y., Cha, B., Lee, J.H., Jang, M., Lee, J., Kim, J., Lee, G.: Speech gesture generation from the trimodal context of text, audio, and speaker identity. ACM Transactions on Graphics (TOG) 39
2020
Earlier work this paper cites.
Bhattacharya, U., Rewkowski, N., Banerjee, A., Guhan, P., Bera, A., Manocha, D.: Text2gestures: A transformer-based network for generating emotive body gestures for virtual agents. In: 2021 IEEE virtual reality and 3D user interfaces (VR). pp. 1–10. IEEE (2021)
2021
Earlier work this paper cites.
Ghosh, A., Cheema, N., Oguz, C., Theobalt, C., Slusallek, P.: Synthesis of compositional animations from textual descriptions. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 1396–1406 (2021)
2021
Cited alongside, same era.
Li, J., Kang, D., Pei, W., Zhe, X., Zhang, Y., He, Z., Bao, L.: Audio2gestures: Generating diverse gestures from speech audio with conditional variational autoencoders. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11293–11302 (2021)
2021
Cited alongside, same era.
Petrovich, M., Black, M.J., Varol, G.: Action-conditioned 3d human motion synthesis with transformer vae. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10985–10995 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
Xu, Y., Zhang, J., Zhang, Q., Tao, D.: Vitpose: Simple vision transformer baselines for human pose estimation. Advances in Neural Information Processing Systems 35
2022
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2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
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2021
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Sinha, A., Song, J., Meng, C., Ermon, S.: D2c: Diffusion-decoding models for few-shot conditional generation. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
Chung, H., Sim, B., Ye, J.C.: Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12413–12422 (2022)
2022
Cited alongside, same era.
Guo, C., Zou, S., Zuo, X., Wang, S., Ji, W., Li, X., Cheng, L.: Generating diverse and natural 3d human motions from text. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5152–5161 (2022)
2022
Cited alongside, same era.
Guo, C., Zuo, X., Wang, S., Cheng, L.: Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts. In: European Conference on Computer Vision. pp. 580–597. Springer (2022)
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11461–11471 (2022)
2022
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2022
Cited alongside, same era.
2023
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2023
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Ren, Z., Pan, Z., Zhou, X., Kang, L.: Diffusion motion: Generate text-guided 3d human motion by diffusion model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2023)
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