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In this paper, we introduce Recon3DMind, a groundbreaking task focused onreconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI)signals. This represents a major step forward in cognitive neuroscience andcomputer vision. To support this task, we present the fMRI-Shape dataset,utilizing 360-degree view videos of 3D objects for comprehensive fMRI signalcapture. Containing 55 categories of common objects from daily life, thisdataset will bolster future research endeavors. We also propose MinD-3D, anovel and effective three-stage framework that decodes and reconstructs thebrain's 3D visual information from fMRI signals. This method starts byextracting and aggregating features from fMRI frames using a neuro-fusionencoder, then employs a feature bridge diffusion model to generatecorresponding visual features, and ultimately recovers the 3D object through agenerative transformer decoder. Our experiments demonstrate that this methodeffectively extracts features that are valid and highly correlated with visualregions of interest (ROIs) in fMRI signals. Notably, it not only reconstructs3D objects with high semantic relevance and spatial similarity but alsosignificantly deepens our understanding of the human brain's 3D visualprocessing capabilities. Project page at: https://jianxgao.github.io/MinD-3D.

MinD-3D: Reconstruct High-quality 3D objects in Human Brain