Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity
This work addresses the challenge of understanding human cognitive processes by enabling high-quality video reconstruction from fMRI data, representing a significant advance over static image methods.
The paper tackles the problem of reconstructing continuous videos from brain activity, achieving an average accuracy of 85% in semantic classification and 0.19 in SSIM, outperforming previous state-of-the-art by 45%.
Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing static images from non-invasive brain recordings, work on recovering continuous visual experiences in the form of videos is limited. In this work, we propose Mind-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention, and co-training with an augmented Stable Diffusion model that incorporates network temporal inflation. We show that high-quality videos of arbitrary frame rates can be reconstructed with Mind-Video using adversarial guidance. The recovered videos were evaluated with various semantic and pixel-level metrics. We achieved an average accuracy of 85% in semantic classification tasks and 0.19 in structural similarity index (SSIM), outperforming the previous state-of-the-art by 45%. We also show that our model is biologically plausible and interpretable, reflecting established physiological processes.