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