We propose a novel framework COLLAGE for generating collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). Our model addresses the lack of rich datasets in this domain by incorporating the knowledge and reasoning abilities of LLMs to guide a generative diffusion model. The hierarchical VQ-VAE architecture captures different motion-specific characteristics at multiple levels of abstraction, avoiding redundant concepts and enabling efficient multi-resolution representation. We introduce a diffusion model that operates in the latent space and incorporates LLM-generated motion planning cues to guide the denoising process, resulting in prompt-specific motion generation with greater control and diversity. Experimental results on the 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. Our work opens up new possibilities for modeling complex interactions in various domains, such as robotics, graphics and computer vision.

本研究针对协作人机交互生成领域数据集不足的问题，提出了COLLAGE框架，利用大语言模型(LLM)和层次化运动特征向量量化变分自编码器(VQ-VAE)。我们的关键发现是，通过潜在空间中的扩散模型结合LLM生成的运动规划线索，可以实现更具控制和多样性的特定运动生成，推动了机器人、图形和计算机视觉等领域的复杂交互建模的新可能。

COLLAGE：基于分层潜在扩散与语言模型的人机协作交互生成