Summary of Collage: Collaborative Human-agent Interaction Generation Using Hierarchical Latent Diffusion and Language Models, by Divyanshu Daiya et al.
COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models
by Divyanshu Daiya, Damon Conover, Aniket Bera
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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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 COLLAGE framework generates collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). The model addresses the lack of rich datasets in this domain by incorporating LLMs to guide a generative diffusion model. The hierarchical VQ-VAE architecture captures different motion-specific characteristics at multiple levels, avoiding redundant concepts and enabling efficient multi-resolution representation. A diffusion model operates in the latent space, incorporating LLM-generated motion planning cues to guide denoising, resulting in prompt-specific motion generation with greater control and diversity. Experimental results on CORE-4D and InterHuman datasets demonstrate the effectiveness of our approach in generating realistic and diverse collaborative human-object-human interactions, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our new framework helps computers create more realistic and varied scenes of people working together with objects. We use a combination of language models and special kinds of autoencoders to generate these scenarios. This allows us to make the scenarios more detailed and diverse, which is important for things like robotics and computer graphics. Our results show that our method works well and can even outperform other state-of-the-art methods. |
Keywords
» Artificial intelligence » Diffusion model » Latent space » Prompt
