Summary of Bad: Bidirectional Auto-regressive Diffusion For Text-to-motion Generation, by S. Rohollah Hosseyni et al.
BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation
by S. Rohollah Hosseyni, Ali Ahmad Rahmani, S. Jamal Seyedmohammadi, Sanaz Seyedin, Arash Mohammadi
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 Bidirectional Autoregressive Diffusion (BAD) model tackles the limitations of traditional autoregressive and mask-based generative models by unifying their strengths. BAD addresses issues such as token independence assumptions, corrupted sequences, and unnatural distortions through a permutation-based corruption technique that preserves natural sequence structure while enforcing causal dependencies. This enables effective capture of both sequential and bidirectional relationships, outperforming existing models in text-to-motion generation tasks. The proposed pre-training strategy for sequence modeling has the potential to improve various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BAD model is a new way to make computers generate text that looks like it was written by humans. It tries to fix some problems with previous methods that use patterns and structures from text data. The new approach makes sure the generated text follows the same rules as real text, which makes it more realistic. This could be useful for things like creating fake news articles or making computers write stories. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Mask » Token
