  Reconstructing visual stimuli from functional Magnetic Resonance Imaging
(fMRI) based on Latent Diffusion Models (LDM) provides a fine-grained retrieval
of the brain. A challenge persists in reconstructing a cohesive alignment of
details (such as structure, background, texture, color, etc.). Moreover, LDMs
would generate different image results even under the same conditions. For
these, we first uncover the neuroscientific perspective of LDM-based methods
that is top-down creation based on pre-trained knowledge from massive images
but lack of detail-driven bottom-up perception resulting in unfaithful details.
We propose NeuralDiffuser which introduces primary visual feature guidance to
provide detail cues in the form of gradients, extending the bottom-up process
for LDM-based methods to achieve faithful semantics and details. We also
developed a novel guidance strategy to ensure the consistency of repeated
reconstructions rather than a variety of results. We obtain the
state-of-the-art performance of NeuralDiffuser on the Natural Senses Dataset
(NSD), which offers more faithful details and consistent results.
