Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
Authors: Zilin Wang, Haolin Zhuang, Lu Li, Yinmin Zhang, Junjie Zhong, Jun Chen, Yu Yang, Boshi Tang, Zhiyong Wu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset. and Empirical experiments on the AIST++ dataset (Li et al. 2021) demonstrate that the proposed E3D2 outperforms the behavior cloning (pure supervised) method across multiple metrics. |
| Researcher Affiliation | Academia | 1Shenzhen International Graduate School, Tsinghua University 2The University of Sydney 3Waseda University |
| Pseudocode | No | The paper describes the methodology steps but does not provide any formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a 'demo page' for visualizations, but does not explicitly state that the source code for the methodology is available or provide a link to a code repository. The arXiv link is for the paper itself, not necessarily the code. |
| Open Datasets | Yes | We conduct the training and experiments on the AIST++ dataset (Li et al. 2021), which is the largest public available dataset for aligned 3D dance motions and music. |
| Dataset Splits | No | The paper states: 'In line with (Li et al. 2021; Siyao et al. 2022), we split these data into 952 sequences for training and 40 sequences for subsequent experiments.' This only specifies a training and testing split, without explicitly mentioning a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Librosa to extract music features' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | No | The paper states, 'More details and hyper-parameter settings can be found in Appendices,' indicating that specific experimental setup details are not present in the main text. |