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Artificial Intelligence-Assisted Simulation Research on Intelligent Behavior of Film and Television 3D Animation Characters

  
18 nov 2024

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Chouinard-Thuly, L., Gierszewski, S., Rosenthal, G. G., Reader, S. M., Rieucau, G., Woo, K. L., ... & Witte, K. (2017). Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Current zoology, 63(1), 5-19. Search in Google Scholar

Dvorožňák, M., Sýkora, D., Curtis, C., Curless, B., Sorkine-Hornung, O., & Salesin, D. (2020). Monster mash: a single-view approach to casual 3D modeling and animation. ACM Transactions on Graphics (ToG), 39(6), 1-12. Search in Google Scholar

Habermann, M., Liu, L., Xu, W., Zollhoefer, M., Pons-Moll, G., & Theobalt, C. (2021). Real-time deep dynamic characters. ACM Transactions on Graphics (ToG), 40(4), 1-16. Search in Google Scholar

Zibrek, K., Kokkinara, E., & McDonnell, R. (2017, September). Don’t stand so close to me: investigating the effect of control on the appeal of virtual humans using immersion and a proximity-based behavioral task. In Proceedings of the ACM symposium on applied perception (pp. 1-11). Search in Google Scholar

Schulz, T., Torresen, J., & Herstad, J. (2019). Animation techniques in human-robot interaction user studies: A systematic literature review. ACM Transactions on Human-Robot Interaction (THRI), 8(2), 1-22. Search in Google Scholar

Starke, S., Hendrich, N., & Zhang, J. (2018). Memetic evolution for generic full-body inverse kinematics in robotics and animation. IEEE Transactions on Evolutionary Computation, 23(3), 406-420. Search in Google Scholar

Sadoughi, N., & Busso, C. (2019). Speech-driven animation with meaningful behaviors. Speech Communication, 110, 90-100. Search in Google Scholar

Min, S., Won, J., Lee, S., Park, J., & Lee, J. (2019). Softcon: Simulation and control of soft-bodied animals with biomimetic actuators. ACM Transactions on Graphics (TOG), 38(6), 1-12. Search in Google Scholar

Daněček, R., Black, M. J., & Bolkart, T. (2022). Emoca: Emotion driven monocular face capture and animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20311-20322). Search in Google Scholar

Pham, H. X., Cheung, S., & Pavlovic, V. (2017). Speech-driven 3D facial animation with implicit emotional awareness: A deep learning approach. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 80-88). Search in Google Scholar

Cudeiro, D., Bolkart, T., Laidlaw, C., Ranjan, A., & Black, M. J. (2019). Capture, learning, and synthesis of 3D speaking styles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10101-10111). Search in Google Scholar

Nakada, M., Zhou, T., Chen, H., Weiss, T., & Terzopoulos, D. (2018). Deep learning of biomimetic sensorimotor control for biomechanical human animation. ACM Transactions on Graphics (TOG), 37(4), 1-15. Search in Google Scholar

Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528-538. Search in Google Scholar

Rempe, D., Luo, Z., Bin Peng, X., Yuan, Y., Kitani, K., Kreis, K., ... & Litany, O. (2023). Trace and pace: Controllable pedestrian animation via guided trajectory diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13756-13766). Search in Google Scholar

Park, S., Ryu, H., Lee, S., Lee, S., & Lee, J. (2019). Learning predict-and-simulate policies from unorganized human motion data. ACM Transactions on Graphics (TOG), 38(6), 1-11. Search in Google Scholar

Peng, X. B., Abbeel, P., Levine, S., & Van de Panne, M. (2018). Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions On Graphics (TOG), 37(4), 1-14. Search in Google Scholar

Ling, H. Y., Zinno, F., Cheng, G., & Van De Panne, M. (2020). Character controllers using motion vaes. ACM Transactions on Graphics (TOG), 39(4), 40-1. Search in Google Scholar

Mourot, L., Hoyet, L., Le Clerc, F., Schnitzler, F., & Hellier, P. (2022, February). A survey on deep learning for skeleton‐based human animation. In Computer Graphics Forum (Vol. 41, No. 1, pp. 122-157). Search in Google Scholar

Lee, K., Lee, S., & Lee, J. (2018). Interactive character animation by learning multi-objective control. ACM Transactions on Graphics (TOG), 37(6), 1-10. Search in Google Scholar

Mousas, C., & Anagnostopoulos, C. N. (2024). Character Animation Scripting Environment. In Encyclopedia of Computer Graphics and Games (pp. 274-285). Cham: Springer International Publishing. Search in Google Scholar

Won, J., Gopinath, D., & Hodgins, J. (2020). A scalable approach to control diverse behaviors for physically simulated characters. ACM Transactions on Graphics (TOG), 39(4), 33-1. Search in Google Scholar

Yuan, Y., Wei, S. E., Simon, T., Kitani, K., & Saragih, J. (2021). Simpoe: Simulated character control for 3d human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7159-7169). Search in Google Scholar

Zakharov V. A. (2022). Efficient Equivalence Checking Technique for Some Classes of Finite-State Machines. Automatic Control and Computer Sciences(7),670-701. Search in Google Scholar

Das Nitish & Panchanathan Aruna Priya. (2021). SD‐SHO: Security‐dominated finite state machine state assignment technique with a satisfactory level of hardware optimization. IET Computers & Digital Techniques(5),372-392. Search in Google Scholar

Basappa B. Kodada,Demian Antony D’Mello & D. K. Santhosh Kumar. (2024). Finite State Automata Based Cryptosystem for Secure Data Sharing and De-duplication in Cloud Computing. SN Computer Science(6),774-774. Search in Google Scholar

Gisela De La Fuente Cortes,Guillermo Espinosa Flores Verdad,Alejandro Díaz Méndez & Victor R. Gonzalez Diaz. (2024). A Non-Linear Successive Approximation Finite State Machine for ADCs with Robust Performance. Electronics(14),2756-2756. Search in Google Scholar

Zhenyi Wang,Ping Yu,Yang Zhao,Ruiyi Zhang,Yufan Zhou,Junsong Yuan & Changyou Chen. (2020). Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions. Proceedings of the AAAI Conference on Artificial Intelligence(07),12281-12288. Search in Google Scholar