We address the challenge of creating 3D assets for household articulated objects from a single image.Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process.These limitations hinder the scalability and practicality for articulated object modeling.In this work, we propose a method to generate articulated objects from a single image.Observing the object in a resting state from an arbitrary view, our method generates an articulated object that is visually consistent with the input image.To capture the ambiguity in part shape and motion posed by a single view of the object, we design a diffusion model that learns the plausible variations of objects in terms of geometry and kinematics.To tackle the complexity of generating structured data with attributes in multiple domains, we design a pipeline that produces articulated objects from high-level structure to geometric details in a coarse-to-fine manner, where we use a part connectivity graph and part abstraction as proxies.Our experiments show that our method outperforms the state-of-the-art in articulated object creation by a large margin in terms of the generated object realism, resemblance to the input image, and reconstruction quality.