Summary of Marvel-40m+: Multi-level Visual Elaboration For High-fidelity Text-to-3d Content Creation, by Sankalp Sinha et al.
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
by Sankalp Sinha, Mohammad Sadil Khan, Muhammad Usama, Shino Sam, Didier Stricker, Sk Aziz Ali, Muhammad Zeshan Afzal
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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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 This paper introduces MARVEL-40M+, a large-scale dataset for generating high-fidelity 3D content from text prompts. The dataset contains 40 million text annotations for over 8.9 million 3D assets, aggregated from seven major datasets. A novel multi-stage annotation pipeline is developed to automatically produce multi-level descriptions using open-source pretrained vision language models (VLMs) and large language models (LLMs). This structure supports both fine-grained 3D reconstruction and rapid prototyping. The authors also develop MARVEL-FX3D, a two-stage text-to-3D pipeline that uses Stable Diffusion and a pretrained image-to-3D network to generate 3D textured meshes within 15 seconds. Evaluation results show that MARVEL-40M+ significantly outperforms existing datasets in annotation quality and linguistic diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand text prompts and create 3D images from them. The authors created a huge dataset with millions of text descriptions for 3D objects, which is much bigger and more detailed than what’s currently available. They also developed a new way to automatically add details to the text descriptions using special computer models. This makes it easier to create accurate and realistic 3D images from text prompts. |
Keywords
* Artificial intelligence * Diffusion
