Ditto: Building Digital Twins of Articulated Objects from Interaction
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Abstract (arXiv)
Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating interactive digital twins of real-world articulated objects, which can be directly imported into virtual environments. We introduce Ditto to learn articulation model estimation and 3D geometry reconstruction of an articulated object through interactive perception. Given a pair of visual observations of an articulated object before and after interaction, Ditto reconstructs part-level geometry and estimates the articulation model of the object. We employ implicit neural representations for joint geometry and articulation modeling. Our experiments show that Ditto effectively builds digital twins of articulated objects in a category-agnostic way. We also apply Ditto to real-world objects and deploy the recreated digital twins in physical simulation. Code and additional results are available at https://ut-austin-rpl.github.io/Ditto
Key facts. Cited by 152 papers in the map (data through 7 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 16 Feb 2022.
Builds on
46 of 63 references.
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Citing papers per year: 2022: 10; 2023: 17; 2024: 34; 2025: 48; 2026: 43.
152 papers.
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- 2026StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction29 Jul 2026
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