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This paper introduces FACT, an agentic framework that uses an evidence-diagnosis-revision loop to reconstruct editable 3D models with high geometric and dynamic fidelity from images. By integrating numerical tools to validate updates and repair collision proxies, the system significantly improves the reliability of contact interactions in articulated assets compared to direct parameter inference methods. This advancement addresses critical gaps in simulating realistic physical responses for robotics and digital twin applications.
FACT: Fidelity-Aware Construction of Articulated Twins
arXiv:2609.37067v1 Announce Type: new Abstract: Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Construction of Articulated Twins), an agentic framework that progressively constructs articulated twins to improve geometry, contact, and dynamic fidelity. The agent drives an evidence--diagnosis--revision loop on a shared editable representation, selecting measurements and model edits using quantitative feedback, while numerical tools execute and validate the updates. It reconstructs editable articulated geometry from images through feature planning, targeted measurements, and diagnostic refinement. On this reference, it repairs collision proxies through task-aware local repartitioning before fidelity-constrained compression. Finally, it constructs response models from passive-response videos, using simulation residuals to guide model revision and constrained physical parameter fitting. Experiments show that FACT improves geometric reconstruction over baselines, enables more reliable interaction with simpler collision proxies, and better reproduces held-out physical responses than direct parameter inference.
Abstract:Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Construction of Articulated Twins), an agentic framework that progressively constructs articulated twins to improve geometry, contact, and dynamic fidelity. The agent drives an evidence--diagnosis--revision loop on a shared editable representation, selecting measurements and model edits using quantitative feedback, while numerical tools execute and validate the updates. It reconstructs editable articulated geometry from images through feature planning, targeted measurements, and diagnostic refinement. On this reference, it repairs collision proxies through task-aware local repartitioning before fidelity-constrained compression. Finally, it constructs response models from passive-response videos, using simulation residuals to guide model revision and constrained physical parameter fitting. Experiments show that FACT improves geometric reconstruction over baselines, enables more reliable interaction with simpler collision proxies, and better reproduces held-out physical responses than direct parameter inference.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2609.37067 [cs.RO]
(or arXiv:2609.37067v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.37067 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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