2023 • In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2023, Volume 4: VISAPP
DUPONT, Elona ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > CVI2
SINGH, Inder Pal ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Fuentes, Laura
ALI, Sk Aziz ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
KACEM, Anis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
GHORBEL, Enjie ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
External co-authors :
no
Language :
English
Title :
You Can Dance! Generating Music-Conditioned Dances on Real 3D Scans.
Publication date :
2023
Event name :
18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023)
Event date :
19-02-2023 to 21-02-2023
Journal title :
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2023, Volume 4: VISAPP
Aksan, E., Kaufmann, M., Cao, P., and Hilliges, O. (2021). A spatio-temporal transformer for 3d human motion prediction. In 2021 International Conference on 3D Vision (3DV), pages 565–574. IEEE.
Dodik, A., Sellán, S., Kim, T., and Phillips, A. (2022). Sex and gender in the computer graphics literature. ACM SIGGRAPH Talks.
Hernandez-Olivan, C. and Beltran, J. R. (2021). Music composition with deep learning: A review. arXiv preprint arXiv:2108.12290.
Huang, R., Hu, H., Wu, W., Sawada, K., Zhang, M., and Jiang, D. (2020). Dance revolution: Long-term dance generation with music via curriculum learning. arXiv preprint arXiv:2006.06119.
Huang, Y., Zhang, J., Liu, S., Bao, Q., Zeng, D., Chen, Z., and Liu, W. (2022). Genre-conditioned long-term 3d dance generation driven by music. In ICASSP 2022-2022 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pages 4858–4862. IEEE.
Kärki, K. (2021). Vocaloid liveness? hatsune miku and the live production of japanese virtual idol concerts. In Researching Live Music, pages 127–140. Focal Press.
Lee, H.-Y., Yang, X., Liu, M.-Y., Wang, T.-C., Lu, Y.-D., Yang, M.-H., and Kautz, J. (2019). Dancing to music. Advances in neural information processing systems, 32.
Li, R., Yang, S., Ross, D. A., and Kanazawa, A. (2021). Learn to dance with aist++: Music conditioned 3d dance generation.
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., and Black, M. J. (2015). SMPL: A skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1–248:16.
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A. A., Tzionas, D., and Black, M. J. (2019). Expressive body capture: 3D hands, face, and body from a single image. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
Pu, J. and Shan, Y. (2022). Music-driven dance regeneration with controllable key pose constraints. arXiv preprint arXiv:2207.03682.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125.
Saint, A., Ahmed, E., Cherenkova, K., Gusev, G., Aouada, D., Ottersten, B., et al. (2018). 3dbodytex: Textured 3d body dataset. In 2018 International Conference on 3D Vision (3DV), pages 495–504. IEEE.
Saint, A., Kacem, A., Cherenkova, K., and Aouada, D. (2020a). 3dbooster: 3d body shape and texture recovery. In European Conference on Computer Vision, pages 726–740. Springer.
Saint, A., Kacem, A., Cherenkova, K., Papadopoulos, K., Chibane, J., Pons-Moll, G., Gusev, G., Fofi, D., Aouada, D., and Ottersten, B. (2020b). Sharp 2020: The 1st shape recovery from partial textured 3d scans challenge results. In European Conference on Computer Vision, pages 741–755. Springer.
Saint, A., Rahman Shabayek, A. E., Cherenkova, K., Gusev, G., Aouada, D., and Ottersten, B. (2019). Body-fitr: Robust automatic 3d human body fitting. In 2019 IEEE International Conference on Image Processing (ICIP).
Siyao, L., Yu, W., Gu, T., Lin, C., Wang, Q., Qian, C., Loy, C. C., and Liu, Z. (2022). Bailando: 3d dance generation by actor-critic gpt with choreographic memory. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11050–11059.
Sun, G., Wong, Y., Cheng, Z., Kankanhalli, M. S., Geng, W., and Li, X. (2020). Deepdance: music-to-dance motion choreography with adversarial learning. IEEE Transactions on Multimedia, 23:497–509.
Tang, T., Jia, J., and Mao, H. (2018a). Dance with melody: An lstm-autoencoder approach to music-oriented dance synthesis. In 2018 ACM Multimedia Conference on Multimedia Conference, pages 1598– 1606. ACM.
Tang, T., Mao, H., and Jia, J. (2018b). Anidance: Real-time dance motion synthesize to the song. In 2018 ACM Multimedia Conference on Multimedia Conference, pages 1237–1239. ACM.
Tiwari, G., Sarafianos, N., Tung, T., and Pons-Moll, G. (2021). Neural-gif: Neural generalized implicit functions for animating people in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11708–11718.
Tsuchida, S., Fukayama, S., Hamasaki, M., and Goto, M. (2019). Aist dance video database: Multi-genre, multi-dancer, and multi-camera database for dance information processing. In Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, pages 501–510, Delft, Netherlands.
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., and Catanzaro, B. (2018). Video-to-video synthesis. Advances in Neural Information Processing Systems, 31.