Climbing Motion Synthesis using Reinforcement Learning
Author:
Funder
과학기술정보통신부
Publisher
Korea Computer Graphics Society
Link
http://journal.cg-korea.org/download/download_pdf?doi=10.15701/kcgs.2024.30.2.21
Reference16 articles.
1. S. Maheshwari, R. Narain, and R. Hebbalaguppe, “Transfer4d: A framework for frugal motion capture and deformation transfer,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12 836–12 846, 2023. 10.1109/CVPR52729.2023.01234
2. X. Yi, Y. Zhou, and F. Xu, “Transpose: Real-time 3d human translation and pose estimation with six inertial sensors,” ACM Transactions on Graphics, vol. 40, no. 4, 2021. 10.1145/3450626.3459786
3. X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Trans. Graph., vol. 37, no. 4, pp. 143:1–143:14, July 2018. 10.1145/3197517.3201311
4. X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: Adversarial motion priors for stylized physics-based character control,” ACM Trans. Graph., vol. 40, no. 4, July 2021. 10.1145/3450626.3459670
5. X. B. Peng, Y. Guo, L. Halper, S. Levine, and S. Fidler, “Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters,” ACM Trans. Graph., vol. 41, no. 4, July 2022. 10.1145/3528223.3530110
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