GPT-Connect: Interaction between Text-Driven Human Motion Generator and
  3D Scenes in a Training-free Manner
Haoxuan Qu Ziyan Guo Jun Liu
Recently, while text-driven human motion generation has received massive
research attention, most existing text-driven motion generators are generally
only designed to generate motion sequences in a blank background. While this is
the case, in practice, human beings naturally perform their motions in 3D
scenes, rather than in a blank background. Considering this, we here aim to
perform scene-aware text-drive motion generation instead. Yet, intuitively
training a separate scene-aware motion generator in a supervised way can
require a large amount of motion samples to be troublesomely collected and
annotated in a large scale of different 3D scenes. To handle this task rather
in a relatively convenient manner, in this paper, we propose a novel
GPT-connect framework. In GPT-connect, we enable scene-aware motion sequences
to be generated directly utilizing the existing blank-background human motion
generator, via leveraging ChatGPT to connect the existing motion generator with
the 3D scene in a totally training-free manner. Extensive experiments
demonstrate the efficacy and generalizability of our proposed framework.