title: VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision
    Understanding
summary: 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
paper_authors._id: 65f3b6d48dc99b4c3a45d1cd
paper_authors.name: Chris Kelly
paper_authors._id: 65f3b6d48dc99b4c3a45d1ce
paper_authors.name: Luhui Hu
paper_authors._id: 65f3b6d48dc99b4c3a45d1cf
paper_authors.name: Jiayin Hu
paper_authors._id: 65f3b6d48dc99b4c3a45d1d0
paper_authors.name: Yu Tian
paper_authors._id: 65f3b6d48dc99b4c3a45d1d1
paper_authors.name: Deshun Yang
paper_authors._id: 65f3b6d48dc99b4c3a45d1d2
paper_authors.name: Bang Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-15T08:46:54.409Z
paper_authors._id: 63e32fbac123238b18a06d5e
paper_authors.avatarUrl: /avatars/a6fab9c4430c7616ce050dab5b819f7e.svg
paper_authors.fullname: Bang Yang
paper_authors.type: user
paper_authors.user: yangbang18
paper_authors._id: 65f3b6d48dc99b4c3a45d1d3
paper_authors.name: Cindy Yang
paper_authors._id: 65f3b6d48dc99b4c3a45d1d4
paper_authors.name: Zihao Li
paper_authors._id: 65f3b6d48dc99b4c3a45d1d5
paper_authors.name: Zaoshan Huang
paper_authors._id: 65f3b6d48dc99b4c3a45d1d6
paper_authors.name: Yuexian Zou
paper_title: VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision
    Understanding
paper_summary: 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
paper_ai_summary: The VisionGPT-3D framework integrates advanced computer vision and large language models to automatically generate optimal 3D visuals from 2D images and text prompts.
paper_ai_keywords: VisionGPT-3D 3D vision understanding multimodal agent