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

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

Coarse-to-Fine Amodal Segmentation with Shape Prior

Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this problem by progressively modeling the amodal segmentation. C2F-Seg initially reduces the learning space from the pixel-level image space to the vector-quantized latent space. This enables us to better handle long-range dependencies and learn a coarse-grained amodal segment from visual features and visible segments. However, this latent space lacks detailed information about the object, which makes it difficult to provide a precise segmentation directly. To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation. To help the studies of amodal object segmentation, we create a synthetic amodal dataset, named as MOViD-Amodal (MOViD-A), which can be used for both image and video amodal object segmentation. We extensively evaluate our model on two benchmark datasets: KINS and COCO-A. Our empirical results demonstrate the superiority of C2F-Seg. Moreover, we exhibit the potential of our approach for video amodal object segmentation tasks on FISHBOWL and our proposed MOViD-A. Project page at: http://jianxgao.github.io/C2F-Seg.