A Simulator-Grounded Framework For Constructing Verifiable Muscle-Grounded QA From 3D Tongue Meshes (extended version)
TL;DR
A simulator-grounded construction framework, instantiated as 3DTongueQA, that shows geometry-grounded supervision supporting both unified language QA and structured prediction and shows geometry-grounded supervision supporting both unified language QA and structured prediction.
Abstract
Existing articulatory corpora based on real-time MRI and electromagnetic articulography capture tongue motion but lack traceable labels for the muscle-driven process underlying each configuration. We introduce a simulator-grounded construction framework, instantiated as 3DTongueQA. Controlled 11-D muscle activations are mapped by the ArtiSynth Badin finite-element model to fixed-topology tongue meshes, converted into reusable biomechanical records, and rendered as deterministic QA on simulator-defined muscle state, geometry, and target-directed change. From 295,157 screened configurations, 295,115 valid meshes yield 891,156 QA records per language. English and Korean naturalizations modify only surface form and are verified against the source records. A swappable SpiralNet++--Qwen3-8B baseline reaches $62.9\pm9.2$ Muscle EM, dropping to 2.2 under mesh shuffling; anchor-held-out training retains 80.4--98.6\% of full-inventory scores, while task-specific readouts reach $88.7\pm0.7$ Muscle EM and $93.3\pm1.0$ Direction EM. The results show geometry-grounded supervision supporting both unified language QA and structured prediction. Code is available at https://github.com/esh0504/muscle-grounded-qa .
