Summary of Mote: Learning Motion-text Diffusion Model For Multiple Generation Tasks, by Yiming Wu et al.
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks
by Yiming Wu, Wei Ji, Kecheng Zheng, Zicheng Wang, Dong Xu
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 This paper presents a unified multi-modal model called MoTe that can handle various tasks by learning the marginal, conditional, and joint distributions of motion and text simultaneously. The MoTe model is composed of three components: Motion Encoder-Decoder (MED), Text Encoder-Decoder (TED), and Moti-on-Text Diffusion Model (MTDM). These components are trained to extract latent embeddings from motion sequences and textual descriptions, which are then used for paired text-motion generation, motion captioning, and text-driven motion generation. Experimental results on benchmark datasets demonstrate the superior performance of MoTe on text-to-motion generation and competitive performance on motion captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoTe is a new model that can make movies with scripts, or create scripts from movie scenes. It’s like a super smart artist that can understand both words and actions. This model is special because it can do many things at once, like making a script for a dance routine or turning a poem into a ballet. |
Keywords
» Artificial intelligence » Diffusion model » Encoder decoder » Multi modal
