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title
Real2Code: Reconstruct Articulated Objects with Code Generation
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Real2Code
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2026-02-14 14:12:03
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Real2Code: Reconstruct Articulated Objects with Code Generation Real2Code: Reconstruct Articulated Objects via Code Generation Mandi Zhao 1 , Yijia Weng 1 , Dominik Bauer 2 , Shuran Song 1,2 1 Stanford University   2 Columbia University Arxiv |   Code |   Data We present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry using an image segmentation model and a shape completion model. We then represent the object parts with oriented bounding boxes, which are input to a fine-tuned large language model (LLM) to predict joint articulation as code. By leveraging pre-trained vision and language models, our approach scales elegantly with the number of articulated parts, and generalizes from synthetic training data to real world objects in unstructured envi...
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