Articulated Object Reconstruction from Rest-State Observation
Summary
This paper introduces a rest-state framework that reconstructs articulated objects from a single closed configuration, using explicit meshes, vision-language outputs, and video diffusion models to generate and validate articulation hypotheses without observed motion.
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Paper page - Articulated Object Reconstruction from Rest-State Observation
Source: https://huggingface.co/papers/2607.27749
Abstract
A rest-state framework reconstructs articulated objects from a single closed configuration by fusing vision-language outputs into consistent part meshes and validating synthesized motion hypotheses via geometric consistency.
Building interactivedigital twinsrequires recovering both 3D geometry and the kinematic structures that govern how objects articulate. Yet existing methods forarticulated object reconstructionrequire explicitly observable motion from multiple articulation states. We introduce arest-state formulationthat reconstructs articulated objects from a single closed configuration, an inherently ill-posed setting where geometry, semantics, and motion priors compensate for the absence of motion cues. Our framework adopts anexplicit meshas an intermediate representation for cross-model verification and fusion, reconciling noisy outputs from vision-language andsegmentation modelsinto spatially consistent part structures. To estimatejoint parameterswithout observed motion, we use avideo diffusion modelto synthesizearticulation hypothesesand validate them throughgeometric consistency. Our approach achieves accurate part decomposition and physically plausible articulation, performing competitively with motion-observing reconstruction-based, generation-based, and modular pretrained-model baselines.
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