Mind Video

Converting brain activity into high-quality videos.

Open AI tool

Product information and features

Discover the power of an AI tool that can turn brain activity into high-quality videos with Mind Video. This innovative platform uses continuous functional magnetic resonance imaging (fMRI) data to recreate continuous visual experiences. Mind Video takes the challenge of extracting visual experiences from non-invasive brain recordings a step further by producing videos instead of static images.

This unique tool consists of a two-module pipeline that seamlessly transitions from image to video brain decoding. The first module harnesses the power of unsupervised learning through masked brain modeling and spatiotemporal attention to learn general visual fMRI features. It further refines these features using a multimodal contrastive learning approach with an annotated dataset.

The second module uses the learned features and fine-tunes them through co-training with an augmented stable diffusion model, specifically developed for video generation guided by fMRI data. Mind Video's significant contribution lies in its adaptable pipeline, which includes an fMRI encoder and an augmented stable diffusion model, trained separately but fine-tuned together.

The tool employs a progressive learning scheme, enabling the encoder to learn brain features through multiple stages. The resulting videos are not only high-quality but also feature high semantic accuracy, including motions and scene dynamics, surpassing previous state-of-the-art models.

Mind Video, found at mind-video.com, makes use of data from the Human Connectome Project. The tool's layers in the fMRI encoder demonstrate a hierarchical nature, extracting structural and abstract visual features. Furthermore, the fMRI encoder continuously improves in assimilating nuanced semantic information throughout its training stages.

In summary, Mind Video is an AI tool that reconstructs videos from brain activity captured through fMRI data. Its main features include a two-module pipeline, the use of unsupervised learning, and a progressive learning scheme. Some of its uses include understanding brain activity, researching human visual experiences, and exploring the intricacies of the visual cortex.

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