Editing articulated objects within a single image requires precise kinematic control—specifying exactly what moves, along which axis, and by how much. Existing text- and drag-based image editing methods provide only weak or ambiguous control over articulated object structure, often failing to localize the correct movable part or execute precise articulation extents, particularly under occlusion. We propose RigIt, a novel framework that enables precise object articulation by directly "rigging" parts within a 2D image. Our method extracts the underlying geometry of a user-selected part to fit a 3D kinematic proxy, represented as an oriented bounding box (OBB). Users interactively manipulate this explicit 3D handle, which the system projects into a 2D wireframe conditioning signal. A fine-tuned diffusion model then processes this signal, guided by a localized articulation-aware loss, to generate photorealistic edits. Remarkably, despite being trained exclusively on a small synthetic dataset of six categories, RigIt generalizes robustly to real-world, in-the-wild images and unseen object classes. Extensive evaluations on both synthetic and real-world benchmarks demonstrate that our approach significantly outperforms state-of-the-art text and drag-based baselines, achieving over 90% user preference for preserving articulation intent and scene consistency.