Summary of Muses: 3d-controllable Image Generation Via Multi-modal Agent Collaboration, by Yanbo Ding et al.
MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration
by Yanbo Ding, Shaobin Zhuang, Kunchang Li, Zhengrong Yue, Yu Qiao, Yali Wang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers present MUSES, a generic AI system that can generate 3D-controllable images from user queries. The system consists of three components: Layout Manager for lifting 2D layouts to 3D, Model Engineer for acquiring and calibrating 3D objects, and Image Artist for rendering 3D scenes into 2D images. This multi-modal agent pipeline mimics human collaboration and enables the automatic creation of complex 3D images with multiple objects. The authors also introduce a new benchmark, T2I-3DisBench, which describes diverse 3D image scenes with detailed prompts. Experiments show MUSES outperforms recent competitors like DALL-E 3 and Stable Diffusion 3 on both benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MUSES is an AI system that can create 3D images from text descriptions. It’s like having a super-powerful artist who can bring your ideas to life in three dimensions! The system has three parts: one for planning, one for designing the objects, and one for turning them into pictures. This makes it really good at creating complex scenes with many objects. The researchers also created a new set of examples that shows how different 3D images can be. They tested MUSES on these examples and found that it does better than other systems. |
Keywords
» Artificial intelligence » Diffusion » Multi modal
