ArtiReal: Upgrading Your Coarse Articulated Models into High-Fidelity Articulated Assets

High-quality articulated 3D assets are increasingly valuable for robotic interaction, physics-based simulation and digital-twin modeling. Recent articulated-object generators can produce structured models with executable articulation, but still face limitations in fine-grained geometry, realistic appearance, and overall visual coherence. Meanwhile, recent static 3D generation models can synthesize highly realistic assets, yet lack explicit motion properties and have not been fully exploited by existing articulated-object generation pipelines. We introduce ArtiReal, a generator-agnostic agentic framework that bridges this gap by upgrading visually coarse articulated assets into high-fidelity ones while preserving their original structure and articulation. To achieve this goal, we first develop an articulation-aware visual evidence planning module that actively selects informative observation configurations and constructs coherent and photorealistic object- and part-level references. We then introduce a structure-preserving adaptive 3D realization module that adaptively composes geometry and appearance operations to produce high-fidelity parts while preserving articulation constraints. Moreover, an action-conditioned provenance critic monitors the entire process, traces failures to their upstream causes, and enables targeted rollback and repair. We evaluate ArtiReal on articulated assets produced by multiple existing generators, with comprehensive comparisons covering visual realism, structural and articulation consistency, efficiency, and systematic ablations. The results demonstrate that ArtiReal consistently improves the visual quality of diverse articulated assets while maintaining their structural and functional validity. Please refer to the appendix and our anonymous supplementary page for more details and demonstrations.