Summary of Vp-llm: Text-driven 3d Volume Completion with Large Language Models Through Patchification, by Jianmeng Liu et al.
VP-LLM: Text-Driven 3D Volume Completion with Large Language Models through Patchification
by Jianmeng Liu, Yichen Liu, Yuyao Zhang, Zeyuan Meng, Yu-Wing Tai, Chi-Keung Tang
First submitted to arxiv on: 8 Jun 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 paper introduces Volume Patch LLM (VP-LLM), a conditional 3D completion model that leverages large language models (LLMs) to perform multi-modal understanding and generation tasks. The approach relies on patching the incomplete 3D object into smaller segments, encoding each segment independently using an LLM, and then injecting semantic meanings based on text prompts. This allows for complex instruction-based 3D completion in a single-forward pass, surpassing state-of-the-art diffusion-based models in terms of generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses large language models (LLMs) to fill gaps in 3D objects based on written instructions. It breaks the object into smaller pieces and then uses the LLMs to understand what each piece should look like according to the text. This helps create more realistic and detailed 3D images. |
Keywords
» Artificial intelligence » Diffusion » Multi modal
