Daedalus: Verifier-Guided Agentic Refinement for Part-Structured 3D Generation

Generating 3D objects as editable mesh parts is essential for animation and interactive modeling, yet producing a decomposition that matches user intent remains challenging. Users must specify not only which parts to generate, but often their spatial extents, while incorrect controls or generated geometry require further correction. We present Daedalus, an agentic framework that connects semantic planning, controllable neural part generation, and verification-guided local refinement. Given a reference image, a part schema, and a whole-object geometric prior, an agent reviews semantic masks on mesh renderings, and geometric tools fit part bounding boxes from the selected surfaces. A mixed-condition generator then synthesizes parts using heterogeneous image, text, bbox, and voxel controls, supporting both individual instances and instance sets as semantic targets. Vision-language-conditioned pretraining establishes the geometric backbone, which is adapted to part generation with shared whole-object context. Verification guides targeted condition revision or part regeneration; candidates are accepted only when checks improve, while accepted non-target geometry is preserved. Experiments on PartObjaverse objects demonstrate improved part-level fidelity under spatial conditioning and benefits from agentic bbox refinement. Qualitative results further illustrate prompt-dependent decompositions and downstream articulation.