Summary of Text-to-3d Gaussian Splatting with Physics-grounded Motion Generation, by Wenqing Wang et al.
Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation
by Wenqing Wang, Yun Fu
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 framework leverages Large Language Model (LLM)-refined prompts and diffusion priors-guided Gaussian Splatting (GS) to generate 3D models with accurate appearances and geometric structures. The approach incorporates a continuum mechanics-based deformation map and color regularization to synthesize vivid physics-grounded motion for the generated 3D Gaussians, adhering to conservation of mass and momentum. This framework integrates text-to-3D generation with physics-grounded motion synthesis, rendering photo-realistic 3D objects that exhibit physics-aware motion. The proposed method achieves high-quality 3D generations with realistic physics-grounded motion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make 3D models and animations using text prompts. Right now, it’s hard to get accurate 3D models from text, and even harder to make them move in a realistic way. The researchers developed a framework that uses large language models and special techniques to generate high-quality 3D models with realistic motion. They tested their approach and showed that it can create photo-realistic 3D objects that behave like real-world objects. |
Keywords
» Artificial intelligence » Diffusion » Large language model » Regularization
