This paper presents MonoArt, a unified framework for reconstructing articulated 3D objects from single images through progressive structural reasoning. The method addresses the challenge of inferring object geometry, part structure, and motion parameters from limited visual evidence without direct articulation regression. By progressively transforming visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture, the framework enables stable and interpretable articulation inference without external templates or multi-stage pipelines. The approach is validated on articulated object datasets demonstrating effective 3D reconstruction of objects with movable parts.