Summary of Large Body Language Models, by Saif Punjwani and Larry Heck
Large Body Language Models
by Saif Punjwani, Larry Heck
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A novel approach is introduced to generate realistic and contextually appropriate gestures in real-time for human-computer interaction. The Large Body Language Models (LBLMs) architecture combines a Transformer-XL large language model with a parallelized diffusion model, using multimodal inputs like text, audio, and video. The LBLM-AVA model incorporates key components such as multimodal-to-pose embeddings, enhanced sequence-to-sequence mapping, temporal smoothing, and attention-based refinement to generate lifelike gestures. Trained on the Allo-AVA dataset, LBLM-AVA achieves state-of-the-art performance in gesture generation, reducing Fréchet Gesture Distance by 30% and improving Fréchet Inception Distance by 25%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Virtual agents are getting better at interacting with humans, but they still struggle to make realistic gestures. To fix this, researchers created a new model called LBLMs (Large Body Language Models). They combined two different AI models: one that’s good with language and another that’s good with images. This new model can use text, audio, and video to generate human-like gestures. It has special parts that help it make the gestures look more realistic and natural. The model was trained on a big dataset called Allo-AVA and did really well in generating lifelike gestures. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Large language model » Transformer
