Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MCM: Multi-condition Motion Synthesis Framework
Authors: Zeyu Ling, Bo Han, Yongkang Wong, Han Lin, Mohan Kankanhalli, Weidong Geng
IJCAI 2024 | Venue PDF | 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 Îēt 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. |