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New method functionalizes 3D models using graph completion

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a novel task called object functionalization, aiming to transform visually plausible but non-functional 3D models into ones that are physically operable. This is achieved by formulating the problem as a graph completion task using a new functional graph representation. A neural Graph Functionalizer (GraFu) model is developed to complete incomplete graphs of non-functional 3D objects, which then drives a geometry realization stage to add necessary structural elements and connectors. This process also rectifies errors in motion data, leading to more physically plausible behavior. The team has created FurFun-233, a dataset of 233 furniture models, to support this research. AI

IMPACT This research could lead to more realistic and interactive 3D assets for simulations, gaming, and virtual environments.

RANK_REASON The cluster contains an arXiv preprint detailing a new research method and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method functionalizes 3D models using graph completion

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The cluster contains an arXiv preprint detailing a new research method and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mingrui Zhao, Sai Raj Kishore Perla, Kai Wang, Sauradip Nag, Duc Anh Nguyen, Jiayi Peng, Ruiqi Wang, Angel X. Chang, Manolis Savva, Ali Mahdavi-Amiri, Hao Zhang ·

    Functionalization via Structure Completion and Motion Rectification

    arXiv:2605.18010v2 Announce Type: replace Abstract: Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to function as intended, such as joints, supports, interi…

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