Summary of Make-it-real: Unleashing Large Multimodal Model For Painting 3d Objects with Realistic Materials, by Ye Fang et al.
Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials
by Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, Dahua Lin
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to creating realistic materials for 3D assets using Multimodal Large Language Models (MLLMs), specifically GPT-4V. The proposed method, Make-it-Real, leverages the capabilities of GPT-4V to recognize and describe materials, allowing the construction of a detailed material library. The system utilizes visual cues and hierarchical text prompts to precisely identify and align materials with corresponding 3D object components. This accurate matching enables the generation of new SVBRDF materials that accurately reflect the original albedo map, resulting in visually authentic representations. Make-it-Real streamlines the integration process into 3D content creation workflows, making it a valuable tool for developers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating more realistic-looking 3D objects and materials using special computer models called Multimodal Large Language Models (MLLMs). The model they’re using is called GPT-4V. Right now, people have to manually add material properties to 3D objects, which can be time-consuming and boring. This new approach uses the MLLMs to recognize and describe materials, making it easier to create realistic-looking objects. It also helps match the right material with the correct part of a 3D object. The result is more authentic-looking materials that are harder to tell apart from real-life objects. |
Keywords
» Artificial intelligence » Gpt
