MindShape: Superquadric-Constrained High-Fidelity 3D Reconstruction from fMRI

Recent advances in neural decoding have enabled the reconstruction of 3D visual content from functional magnetic resonance imaging (fMRI) signals, opening a route toward brain-conditioned 3D generation. Nevertheless, existing approaches largely rely on semantic or multi-view priors without explicitly modeling 3D geometric constraints, often leading to structural distortions and degraded geometric fidelity. To address this limitation, we propose MindShape, a geometry-aware neural decoding framework for fMRI-conditioned 3D reconstruction. Unlike previous methods, MindShape decodes superquadric geometry as an explicit, low-dimensional structural constraint and jointly conditions the 3D generation process on both geometric primitives and semantic descriptions, enabling semantically consistent and geometrically faithful reconstructions. Experiments show that MindShape improves reconstruction quality in terms of geometric consistency and semantic fidelity over existing baselines. These results suggest that explicit geometric primitives provide an effective structured interface between noisy neural measurements and controllable 3D generation.