Real2Code: Reconstruct Articulated Objects via Code Generation

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 environments. Experimental results demonstrate that Real2Code significantly outperforms previous state-ofthe-art in reconstruction accuracy, and is the first approach to extrapolate beyond objects' structural complexity in the training set, and reconstructs objects with up to 10 articulated parts. When incorporated with a stereo reconstruction model, Real2Code also generalizes to real world objects from a handful of multi-view RGB images, without the need for depth or camera information. 1

In: Unstructured RGBs Out: Interactable Digital Twins

Real2Code

Figure 1: We propose a novel method for reconstructing articulated objects via code generation, leveraging pre-trained large language models (LLMs). Real2Code takes visual observations as input, and performs both part-level geometry reconstruction and joint prediction. When evaluated on an extensive set of real and synthetic objects with varying level of kinematic complexity, Real2Code can reconstruct complex articulated objects with up to 10 parts, and generalize to real world objects from a handful of pose-free RGB images.