Papers › MCM: Multi-condition Motion Synthesis Framework
MCM: Multi-condition Motion Synthesis Framework
Zeyu Ling, Bo Han, Yongkang Wongkan, Han Lin, Mohan Kankanhalli, Weidong Geng
Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employed as HMS control conditions. While existing research has primarily focused on single conditions, the multi-condition human motion synthesis remains underexplored. In this study, we propose a multi-condition HMS framework, termed MCM, based on a dual-branch structure composed of a main branch and a control branch. This framework effectively extends the applicability of the diffusion model, which is initially predicated solely on textual conditions, to auditory conditions. This extension encompasses both music-to-dance and co-speech HMS while preserving the intrinsic quality of motion and the capabilities for semantic association inherent in the original model. Furthermore, we propose the implementation of a Transformer-based diffusion model, designated as MWNet, as the main branch. This model adeptly apprehends the spatial intricacies and inter-joint correlations inherent in motion sequences, facilitated by the integration of multi-wise self-attention modules. Extensive experiments show that our method achieves competitive results in single-condition and multi-condition HMS tasks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Motion Synthesis | HumanML3D | MCM | Diversity | 9.585 | #8 of 37 | Archive leaderboard | report |
| Motion Synthesis | HumanML3D | MCM | FID | 0.053 | #8 of 37 | Archive leaderboard | report |
| Motion Synthesis | HumanML3D | MCM | Multimodality | 0.8104 | #8 of 37 | Archive leaderboard | report |
| Motion Synthesis | HumanML3D | MCM | R Precision Top3 | 0.788 | #8 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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