Automatic Dance Video Segmentation for Understanding Choreography

Author:

Endo Koki1ORCID,Tsuchida Shuhei2ORCID,Fukusato Tsukasa3ORCID,Igarashi Takeo1ORCID

Affiliation:

1. The University of Tokyo, Japan

2. Ochanomizu University, Japan

3. Waseda University, Japan

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Publisher

ACM

Reference19 articles.

1. Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271 (2018), 1–14. https://arxiv.org/abs/1803.01271

2. Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2016. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). In Conference Track Proceedings of 4th International Conference on Learning Representations (ICLR). San Juan, Puerto Rico, 1–14. http://arxiv.org/abs/1511.07289

3. Temporal convolutional networks for musical audio beat tracking

4. AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

5. Minoru Fujimoto, Tsutomu Terada, and Masahiko Tsukamoto. 2012. A Dance Training System that Maps Self-Images onto an Instruction Video. In Proceedings of 5th International Conference on Advances in Computer-Human Interactions (ACHI). IARIA, Valencia, Spain, 309–314.

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