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..
UniMuMo: Unified Text, Music, and Motion Generation
Authors: Han Yang, Kun Su, Yutong Zhang, Jiaben Chen, Kaizhi Qian, Gaowen Liu, Chuang Gan
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Uni Mu Mo achieves competitive results on all unidirectional generation benchmarks across music, motion, and text modalities. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong, 2University of Washington, 3The University of British Columbia 4University of Massachusetts Amherst, 5MIT-IBM Watson AI Lab, 6Cisco Research |
| Pseudocode | No | The paper describes the model architecture and pipeline in prose, for example: "Our pipeline consists of three main stages: a music-motion joint tokenizer that encodes music and motion sequences into discrete representations within the same space, a music-motion transformer-decoder model trained on the task of music-motion joint generation, and a music-motion captioner that generates text descriptions from music and motion features." It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/hanyangclarence/Uni Mu Mo |
| Open Datasets | Yes | With the augmented synchronized music-motion data, we can utilize existing music and motion datasets to train our unified generative model... Music4All dataset... AIST++ dataset... Music QA dataset released by (Liu et al. 2023b)... Human ML3D test set. |
| Dataset Splits | No | The paper states: "More implementation details about hyperparameter choices, dataset, metrics and training/evaluation setups are in Appendix." While it mentions |
| Hardware Specification | No | The paper states: "More implementation details about hyperparameter choices, dataset, metrics and training/evaluation setups are in Appendix." However, no specific hardware details (like GPU or CPU models) are provided in the main text. |
| Software Dependencies | No | The paper mentions "Demucs (D efossez 2021; Rouard, Massa, and D efossez 2023)" as a tool used, but does not provide specific version numbers for it or any other software dependencies. It also states: "More implementation details about hyperparameter choices, dataset, metrics and training/evaluation setups are in Appendix." |
| Experiment Setup | Yes | Empirically, ฮป is set to 0.02... Empirically, ยต is set to 0.85... More implementation details about hyperparameter choices, dataset, metrics and training/evaluation setups are in Appendix. |