Summary of Real2code: Reconstruct Articulated Objects Via Code Generation, by Zhao Mandi et al.
Real2Code: Reconstruct Articulated Objects via Code Generation
by Zhao Mandi, Yijia Weng, Dominik Bauer, Shuran Song
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents Real2Code, a novel approach to reconstructing articulated objects via code generation. The method involves reconstructing part geometry using image segmentation and shape completion models, then representing these parts as oriented bounding boxes inputted into a fine-tuned large language model (LLM) to predict joint articulation as code. Leveraging pre-trained vision and language models enables the approach to scale elegantly with the number of articulated parts and generalize from synthetic training data to real-world objects in unstructured environments. Experimental results demonstrate significant improvements over previous state-of-the-art methods in reconstruction accuracy, with Real2Code extrapolating beyond objects’ structural complexity in the training set and reconstructing objects with up to 10 articulated parts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real2Code is a new way to rebuild complex objects using computer code. It starts by breaking down an object into its different parts using images and shape completion models. Then, it uses these part representations as input for a special language model that predicts how the parts move together. This approach works well even with many moving parts and can be used in real-world situations without needing special equipment like depth sensors. |
Keywords
» Artificial intelligence » Image segmentation » Language model » Large language model
