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KinemaForge pipeline generates accurate digital twins from sensor data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed KinemaForge, a new pipeline for creating simulation-ready digital twins of articulated objects from RGB-D sensor data. This system jointly infers part geometry, joint topology, and kinematic parameters, addressing previous limitations where these aspects were handled separately. KinemaForge also incorporates an energy-consistent verifier to ensure the reconstructed models adhere to physical laws, reducing simulation drift and improving manipulation success rates compared to existing methods. AI

IMPACT This research could lead to more accurate and stable physics simulations for robotics and virtual environments.

RANK_REASON The cluster contains an academic paper detailing a new method and results.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

KinemaForge pipeline generates accurate digital twins from sensor data

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Xinze Zhang ·

    URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification

    arXiv:2606.18861v1 Announce Type: cross Abstract: Reconstructing simulation-ready digital twins of articulated objects from sensor observations remains constrained by two persistent gaps: (i) part-level geometric reconstruction is decoupled from kinematic-parameter estimation, an…

  2. arXiv cs.CV TIER_1 English(EN) · Xinze Zhang ·

    URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification

    Reconstructing simulation-ready digital twins of articulated objects from sensor observations remains constrained by two persistent gaps: (i) part-level geometric reconstruction is decoupled from kinematic-parameter estimation, and (ii) the recovered models often violate basic dy…

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