Controllable Mind Visual Diffusion Model

TL;DR

This paper introduces the Controllable Mind Visual Diffusion Model (CMVDM), a novel method that enhances brain signal visualization by extracting semantic and silhouette information from fMRI data for more accurate image reconstruction.

Contribution

The paper presents a new diffusion model that incorporates attribute alignment, assistant networks, and residual blocks to improve semantic and silhouette extraction from brain signals.

Findings

CMVDM outperforms existing methods in image reconstruction quality.

The model effectively captures semantic and silhouette features from fMRI data.

Generated images closely match the original visual stimuli.

Abstract

Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Although diffusion models have shown promise in analyzing functional magnetic resonance imaging (fMRI) data, including reconstructing high-quality images consistent with original visual stimuli, their accuracy in extracting semantic and silhouette information from brain signals remains limited. In this regard, we propose a novel approach, referred to as Controllable Mind Visual Diffusion Model (CMVDM). CMVDM extracts semantic and silhouette information from fMRI data using attribute alignment and assistant networks. Additionally, a residual block is incorporated to capture information beyond semantic and silhouette features. We then leverage a control model to fully exploit the extracted information for image synthesis, resulting in generated images that closely resemble the visual stimuli in terms of semantics and silhouette. Through extensive experimentation, we demonstrate that CMVDM outperforms existing state-of-the-art methods both qualitatively and quantitatively.