Scalable Motion Style Transfer with Constrained Diffusion Generation
Authors: Wenjie Yin, Yi Yu, Hang Yin, Danica Kragic, Mårten Björkman
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our validation demonstrates the success of training separate models to transfer between as many as ten dance motion styles. Comprehensive experiments find a significant improvement in preserving motion contents in comparison to baseline and ablative diffusionbased style transfer models. In addition, we perform a human study for a subjective assessment of the quality of generated dance motions. |
| Researcher Affiliation | Academia | 1KTH Royal Institute of Technology, Sweden 2National Institute of Informatics, Japan 3University of Copenhagen, Denmark |
| Pseudocode | Yes | Algorithm 1: Motion style transfer with DDIBs |
| Open Source Code | Yes | The code and summary are available at https: //github.com/YIN95/ddst motion. |
| Open Datasets | Yes | We evaluate our system on the 100STYLE (Mason, Starke, and Komura 2022) locomotion database and the AIST++ (Tsuchida et al. 2019) dance database. |
| Dataset Splits | No | The paper does not provide explicit training/test/validation dataset splits with percentages or sample counts. It mentions using '150-frame clips for experiments' and '90 dance sequences for each style' for evaluation, but not how the data was partitioned for training, validation, and testing. |
| Hardware Specification | No | The paper mentions benefiting from access to 'HPC resources provided by the Swedish National Infrastructure for Computing (SNIC)' but does not provide specific hardware details such as exact GPU/CPU models or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions using software components like 'Jukebox model', 'CLIP', and 'RoBERTa', and 'Conformer' but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | No | The paper provides details on data preparation (e.g., 'downsample both motion datasets to 30 fps and use 150-frame clips') and data representation but does not specify key experimental setup details such as learning rates, batch sizes, number of epochs, or optimizer settings. |