Summary of Grounded Compositional and Diverse Text-to-3d with Pretrained Multi-view Diffusion Model, by Xiaolong Li et al.
Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model
by Xiaolong Li, Jiawei Mo, Ying Wang, Chethan Parameshwara, Xiaohan Fei, Ashwin Swaminathan, CJ Taylor, Zhuowen Tu, Paolo Favaro, Stefano Soatto
First submitted to arxiv on: 28 Apr 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 The proposed Grounded-Dreamer approach generates high-fidelity 3D assets that accurately follow complex text prompts. This two-stage method uses a pre-trained multi-view diffusion model, such as MVDream, with score distillation sampling (SDS). The approach addresses the limitation of existing methods by introducing an attention refocusing mechanism and hybrid optimization strategy. The results show consistent outperformance of previous state-of-the-art methods in terms of quality and accuracy, enabling diverse 3D generation from a single text prompt. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make detailed 3D objects that match what someone says. It’s like taking a picture, but instead of a photo, it’s a 3D model. The new method is called Grounded-Dreamer and uses a special computer program to make the models. This program takes hints from text descriptions to create the 3D objects. The results are very good and show that this method can work well for making complex objects. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Distillation » Optimization » Prompt
