DualCraft: Dual-Workspace Generation of Articulated Assets from Images

Interactive articulated objects such as doors and home appliances are crucial in robotics, but creating their simulation-ready assets remains labor-intensive. Generating them from images is hard because part geometry and motion structure must be recovered jointly: refining geometry changes assembly positions and clearances, while motion errors may require geometric fixes. We present DualCraft, a dual-workspace agent that models articulated objects from images without additional training. Its geometry workspace refines part shapes and materials against the images, and its program workspace defines the assembly and joints and checks their motion; shared part frames keep the two consistent. We further introduce RealProduct, 114 assets of 63 product types, each modeled by hand after a specific real product, with physically based materials and the interiors that motion reveals, and combine it with two recent collections into ARTS-Bench, a 690-object benchmark that scores multiview appearance and part geometry at rest and in motion. DualCraft achieves the best score on every ARTS-Bench metric against six baselines, reducing Chamfer distance in articulated states by 46% relative to a coding agent that uses the same model, and ablations show that removing its shared frames, visual feedback, or geometry checks each more than doubles Chamfer distance at rest. We further demonstrate that the generated assets can be used directly in simulated robot manipulation tasks that go beyond basic pick and place, and that trajectories planned with them in simulation transfer unchanged to a real robot. We will release the code, dataset, and benchmark.