Abstract
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.
Abstract (translated)
我们提出了一个名为COLLAGE的新框架,通过利用大型语言模型(LLMs)和分层运动特定向量量化变分自编码器(VQ-VAEs)来生成协同机器人-对象-机器人交互。我们的模型通过结合LLMs的知识和推理能力来指导生成扩散模型。分层的VQ-VAE架构捕捉了多个抽象层次的不同的运动相关特征,避免了冗余概念,并实现了高效的多分辨率表示。我们引入了一个在潜在空间中操作的扩散模型,它包含了LLM生成的运动规划提示,指导去噪过程,从而实现了具有更强的控制和多样性的 prompt-specific 运动生成。在CORE-4D 和 InterHuman 数据集上的实验结果证明了我们在生成现实和多样的人-对象-人类交互方面的有效性,超过了最先进的方法。我们的工作为在各种领域建模复杂的交互提供了新的可能性,包括机器人学、图形学和计算机视觉。
URL
https://arxiv.org/abs/2409.20502