Automated Author ProfileLiu, Fan
Hohai University
Liu, Fan
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This repository is the official implementation of “Self-Supervised Music-Motion Synchronization Learning for Music-Driven Conducting Motion Generation”, by Fan Liu, Delong Chen, Ruizhi Zhou, Sai Yang, and Feng Xu. This repository also provide the access to the ConductorMotion100 dataset, which consists of 100 hours of orchestral conductor motions and aligned music Mel spectrogram.The above figure gives a high-level illustration of the proposed two-stage approach. The contrastive learning and generative learning stage are bridged by transferring learned music and motion encoders, as noted in dotted lines. Our approach can generate plausible, diverse, and music-synchronized conducting motion.
Authors
- Liu, Fan ;
- Chen, De-Long ;
- Zhou, Rui-Zhi ;
- Yang, Sai ;
- Xu, Feng