Summary of Mcm: Multi-condition Motion Synthesis Framework, by Zeyu Ling et al.
MCM: Multi-condition Motion Synthesis Framework
by Zeyu Ling, Bo Han, Yongkang Wongkan, Han Lin, Mohan Kankanhalli, Weidong Geng
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MCM framework for conditional human motion synthesis (HMS) extends the applicability of diffusion models to auditory conditions, enabling music-to-dance and co-speech motion generation while preserving quality and semantic association. The dual-branch structure combines a main branch based on Transformer-based MWNet with multi-wise self-attention modules and a control branch. This framework achieves competitive results in single-condition and multi-condition HMS tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to make computers create human-like movements, like dancing or speaking, based on text or music. They created a special kind of computer model that can understand the relationship between body parts and how they move together. This model is good at making realistic dance moves or matching words with gestures. |
Keywords
» Artificial intelligence » Diffusion » Self attention » Transformer
