MeshQuery: Agentic Seam Planning for UV Parameterization

We present MESHQUERY, a training-free agentic approach to automatic UV un- wrapping of production-grade quad meshes. A Vision-Language Model (VLM) plans artist-aligned seams using a set of edge-selection tools, conditioned on domain-specific UV-unwrapping knowledge expressed in natural language and refined with a feedback loop. We design a queryable mesh representation together with a domain-specific language (DSL) that enables the agent to retrieve mesh in- formation on demand, express a seam plan as a compact program of edge-selection operators over topological, geometric, and semantic mesh attributes, and iteratively refine it from UV quality feedback. On Adobe Substance 3D and Toys4K meshes, MESHQUERY produces 2.9×/4.29× fewer charts and 1.63×/1.7× shorter seams than the strongest baseline, and professional artists prefer its results in 80.9% of comparisons. Ultimately, decoupling high-level intent planning from low-level edge selection and compact mesh representation lets MESHQUERY run on different backend VLM and scale to meshes an order of magnitude larger than autoregressive seam prediction