Brain-Streams: fMRI-to-Image Reconstruction with Multi-modal Guidance
<|reference_start|>Brain-Streams: fMRI-to-Image Reconstruction with Multi-modal Guidance: Understanding how humans process visual information is one of the crucial steps for unraveling the underlying mechanism of brain activity. Recently, this curiosity has motivated the fMRI-to-image reconstruction task; given the fMRI data from visual stimuli, it aims to reconstruct the corresponding visual stimuli. Surprisingly, leveraging powerful generative models such as the Latent Diffusion Model (LDM) has shown promising results in reconstructing complex visual stimuli such as high-resolution natural images from vision datasets. Despite the impressive structural fidelity of these reconstructions, they often lack details of small objects, ambiguous shapes, and semantic nuances. Consequently, the incorporation of additional semantic knowledge, beyond mere visuals, becomes imperative. In light of this, we exploit how modern LDMs effectively incorporate multi-modal guidance (text guidance, visual guidance, and image layout) for structurally and semantically plausible image generations. Specifically, inspired by the two-streams hypothesis suggesting that perceptual and semantic information are processed in different brain regions, our framework, Brain-Streams, maps fMRI signals from these brain regions to appropriate embeddings. That is, by extracting textual guidance from semantic information regions and visual guidance from perceptual information regions, Brain-Streams provides accurate multi-modal guidance to LDMs. We validate the reconstruction ability of Brain-Streams both quantitatively and qualitatively on a real fMRI dataset comprising natural image stimuli and fMRI data.<|reference_end|>
@article{joo2024brain-streams:,
    title={Brain-Streams: fMRI-to-Image Reconstruction with Multi-modal Guidance},
    author={Jaehoon Joo, Taejin Jeong, Seongjae Hwang},
    journal={arXiv preprint arXiv:2409.12099},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12099},
    primaryClass={cs.CV}
}