Summary of A Unified Framework For Motion Reasoning and Generation in Human Interaction, by Jeongeun Park et al.
A Unified Framework for Motion Reasoning and Generation in Human Interaction
by Jeongeun Park, Sungjoon Choi, Sangdoo Yun
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces VIM, a large language model that can understand, generate, and control interactive motions in multi-turn conversational contexts. Building on recent advancements in natural language processing, VIM integrates both language and motion modalities to handle diverse interactive scenarios. The model is trained on Inter-MT2, a new dataset containing 82.7K instructions and 153K interactive motion samples. Evaluations across multiple tasks, including text-to-motion, motion-to-text, reaction generation, and reasoning about motion sequences, demonstrate the versatility of VIM in understanding and generating interactive motions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special AI that can understand and make up human-like movements with others. It’s like a super smart robot that can have conversations and even edit or answer questions. The new AI is called VIM, and it uses two types of data: language (like words) and motion (like dance moves). To train the AI, the researchers created a huge dataset with many examples of instructions and movements. They tested the AI on different tasks like turning text into movements, making up new movements from scratch, and even understanding complex movement sequences. |
Keywords
» Artificial intelligence » Large language model » Natural language processing
