MCM: Multi-condition Motion Synthesis Framework
Authors: Zeyu Ling, Bo Han, Yongkang Wong, Han Lin, Mohan Kankanhalli, Weidong Geng
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method achieves competitive results in single-condition and multi-condition HMS tasks. |
| Researcher Affiliation | Academia | Zeyu Ling1 , Bo Han1 , Yongkang Wong2, Han Lin1, Mohan Kangkanhalli2 and Weidong Geng1 1College of Computer Science and Technology, Zhejiang University 2School of Computing, National University of Singapore |
| Pseudocode | No | The paper contains architectural diagrams (Figure 2, Figure 3) but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We evaluate the proposed method using the Human ML3D [Guo et al., 2022a], AIST++ [Li et al., 2021], and BEAT [Liu et al., 2022] dataset. |
| Dataset Splits | No | In the evaluation stage, prior studies were assessed using the complete dance motion sequences from the validation and test sets of AIST++ dataset... Table 1: Quantitative results on the Human ML3D test set. While validation and test sets are mentioned, specific details like percentages or sample counts for train/validation/test splits are not provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | We use CLIP [Radford et al., 2021] to extract features from text conditions. Similar to EDGE, we use the prior layer of Jukebox [Dhariwal et al., 2020] to extract features from all audio conditions... We employ the Adam optimizer for training the model... No specific version numbers for general software dependencies are provided. |
| Experiment Setup | Yes | Regarding the diffusion model, we set the number of diffusion steps at 1000, while the variances βt follow a linear progression from 0.0001 to 0.02. We employ the Adam optimizer for training the model, employing a learning rate of 0.0002 throughout both training phases. In concordance with MDM, we adopt the strategy of predicting xstart as an alternative to the prediction of noise. |