Summary of Visiongpt-3d: a Generalized Multimodal Agent For Enhanced 3d Vision Understanding, by Chris Kelly et al.
VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding
by Chris Kelly, Luhui Hu, Jiayin Hu, Yu Tian, Deshun Yang, Bang Yang, Cindy Yang, Zihao Li, Zaoshan Huang, Yuexian Zou
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed VisionGPT-3D framework unifies state-of-the-art computer vision models and large language models (LLMs) to facilitate the development of vision-oriented AI. Building upon the strengths of multimodal foundation models, VisionGPT-3D integrates various SOTA vision models and automates the selection of suitable 3D mesh creation algorithms for optimal results based on diverse multimodal inputs such as text prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The VisionGPT-3D framework combines powerful computer vision models with large language models to create a new way to generate images and videos from text. This allows for more realistic and accurate image generation, video creation, and object detection. The framework brings together the best of both worlds, using the strengths of each to produce better results. |
Keywords
» Artificial intelligence » Image generation » Object detection
