  The evolution of text to visual components facilitates people's daily lives,
such as generating image, videos from text and identifying the desired elements
within the images. Computer vision models involving the multimodal abilities in
the previous days are focused on image detection, classification based on
well-defined objects. Large language models (LLMs) introduces the
transformation from nature language to visual objects, which present the visual
layout for text contexts. OpenAI GPT-4 has emerged as the pinnacle in LLMs,
while the computer vision (CV) domain boasts a plethora of state-of-the-art
(SOTA) models and algorithms to convert 2D images to their 3D representations.
However, the mismatching between the algorithms with the problem could lead to
undesired results. In response to this challenge, we propose an unified
VisionGPT-3D framework to consolidate the state-of-the-art vision models,
thereby facilitating the development of vision-oriented AI. VisionGPT-3D
provides a versatile multimodal framework building upon the strengths of
multimodal foundation models. It seamlessly integrates various SOTA vision
models and brings the automation in the selection of SOTA vision models,
identifies the suitable 3D mesh creation algorithms corresponding to 2D depth
maps analysis, generates optimal results based on diverse multimodal inputs
such as text prompts.
  Keywords: VisionGPT-3D, 3D vision understanding, Multimodal agent
