Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance

Understanding the neural basis of three-dimensional (3D) perception is a fundamental objective in cognitive neuroscience. Despite advances in decoding 2D visual stimuli from neural data, reconstructing high-fidelity 3D objects with detailed texture and geometry remains largely unexplored. In this work, we introduce NeuroSculptor3D, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity. NeuroSculptor3D integrates a viewpoint-aware brain embedding module that captures fine-grained spatial variations across visual perspectives, and a hierarchical guidance mechanism that aligns brain-derived features with perceptual, semantic, and structural priors. Together, these components facilitate the generation of consistent multi-view embeddings, which are then decoded via TRELLIS to produce high-quality textured 3D reconstructions. Experiments on the fMRI-Shape dataset demonstrate that NeuroSculptor3D outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency. Code will be released to facilitate further research.

Authors

• Xun Zhang Meituan
• Weihao Xia Harbin Engineering University
• Yulong Liu National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University
• Bo Yang Information Engineering University
• Alessandro Bozzon
• Pan Wang

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