GENA3D: Generative Amodal 3D Modeling by Bridging 2D Priors and 3D Coherence

Generating complete 3D objects under partial occlusions (i.e.,amodal scenarios) is a practically important yet challenging problem,as large portions of object geometry are unobserved in real-world sce-narios. Existing approaches either operate directly in 3D, which en-sures geometric consistency but often lacks generative expressiveness,or rely on 2D amodal completion, which provides strong appearancepriors but does not guarantee reliable 3D structure. This raises a keyquestion: how can we achieve both generative plausibility and geometriccoherence in amodal 3D modeling? To answer this question, we intro-duce GENA3D (GENerative Amodal 3D), a framework that integrateslearned 2D generative priors with explicit 3D geometric reasoning withina conditional 3D generation paradigm. The 2D priors enable the modelto plausibly infer diverse occluded content, while the 3D representationenforces multi-view consistency and spatial validity. Our design incorpo-rates a novel View-Wise Cross-Attention for multi-view alignment and aStereo-Conditioned Cross-Attention to anchor generative predictions in3D relationships. By combining generative imagination with structuralconstraints, GENA3D generates complete and coherent 3D objects fromlimited observations without sacrificing geometric fidelity. Experimentsdemonstrate that our method outperforms existing approaches in bothsynthetic and real-world amodal scenarios, highlighting the effectivenessof bridging 2D priors and 3D coherence in generating plausible and geo-metrically consistent 3D structures in complex environments.