Mind-Blowing Dream-To-Video Could Be Coming With Stable Diffusion Video Rebuild From Brain Activity
In this groundbreaking video, we delve into the realm of mind-video and brain-activity reconstruction, bringing you an in-depth discussion on a new research paper titled, "Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity". This may open the doors of dream-to-video era.
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Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️
Stable Diffusion Tutorials, Automatic...
Research Paper ⤵️
https://arxiv.org/pdf/2305.11675.pdf
Video Footage Source ⤵️
https://mind-video.com/
This fascinating research explores the intersection of neurology, machine learning and video generation, aiming to understand and recreate the visual experiences directly from brain signals. Using advanced techniques such as masked brain modeling, multimodal contrastive learning and co-training with an augmented Stable Diffusion model, the MinD-Video approach seeks to convert functional Magnetic Resonance Imaging (fMRI) data into high-quality videos.
We dissect the various components of the MinD-Video methodology, focusing on the fMRI encoder and the video generative model. We also discuss the paper's innovative use of progressive learning and explain the pre-processing of the fMRI data for efficient results.
Further, we explore how the research attempts to address the challenges of time delays and individual variations in brain activity. We go in depth into each stage of the progressive learning applied to the fMRI encoder, from general to semantic-related features and from large-scale pre-training to contrastive learning.
Discover how the Stable Diffusion model is adapted for video generation, and how scene-dynamic sparse causal attention ensures smooth video transitions. We also cover the use of adversarial guidance in controlling the diversity of generated videos and how attention maps help visualize the learning process.
Perfect for anyone interested in neuroscience, machine learning or video generation, this video provides a comprehensive overview of a cutting-edge approach in brain-activity reconstruction. Expand your knowledge and join the discussion as we explore the future of mind-video.
For a more detailed understanding, the link to the full research paper is provided in the description. Stay curious, keep learning, and don't forget to like, comment, and subscribe for more exciting content.
Abstract
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.
Introduction
Life unfolds like a film reel, each moment seamlessly transitioning into the next, forming a “perpetual theater” of experiences. This dynamic narrative forms our perception, explored through the naturalistic paradigm, painting the brain as a moviegoer engrossed in the relentless film of experience. Understanding the information hidden within our complex brain activities is a big puzzle in cognitive neuroscience. The task of recreating human vision from brain recordings, especially using non-invasive tools like functional Magnetic Resonance Imaging (fMRI), is an exciting but difficult task. Non-invasive methods, while less intrusive, capture limited information, susceptible to various interferences like noise. Furthermore, the acquisition of neuroimaging data is a complex, costly process. Despite these complexities, progress has been made, notably in learning valuable fMRI features with limited fMRI-annotation pairs.
#MinDVideo #fMRI
If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 ⤵️
Patreon: SECourses
Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️
Technology & Science: Tutorials, Tips...
Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️
Stable Diffusion Tutorials, Automatic...
Research Paper ⤵️
https://arxiv.org/pdf/2305.11675.pdf
Video Footage Source ⤵️
https://mind-video.com/
This fascinating research explores the intersection of neurology, machine learning and video generation, aiming to understand and recreate the visual experiences directly from brain signals. Using advanced techniques such as masked brain modeling, multimodal contrastive learning and co-training with an augmented Stable Diffusion model, the MinD-Video approach seeks to convert functional Magnetic Resonance Imaging (fMRI) data into high-quality videos.
We dissect the various components of the MinD-Video methodology, focusing on the fMRI encoder and the video generative model. We also discuss the paper's innovative use of progressive learning and explain the pre-processing of the fMRI data for efficient results.
Further, we explore how the research attempts to address the challenges of time delays and individual variations in brain activity. We go in depth into each stage of the progressive learning applied to the fMRI encoder, from general to semantic-related features and from large-scale pre-training to contrastive learning.
Discover how the Stable Diffusion model is adapted for video generation, and how scene-dynamic sparse causal attention ensures smooth video transitions. We also cover the use of adversarial guidance in controlling the diversity of generated videos and how attention maps help visualize the learning process.
Perfect for anyone interested in neuroscience, machine learning or video generation, this video provides a comprehensive overview of a cutting-edge approach in brain-activity reconstruction. Expand your knowledge and join the discussion as we explore the future of mind-video.
For a more detailed understanding, the link to the full research paper is provided in the description. Stay curious, keep learning, and don't forget to like, comment, and subscribe for more exciting content.
Abstract
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.
Introduction
Life unfolds like a film reel, each moment seamlessly transitioning into the next, forming a “perpetual theater” of experiences. This dynamic narrative forms our perception, explored through the naturalistic paradigm, painting the brain as a moviegoer engrossed in the relentless film of experience. Understanding the information hidden within our complex brain activities is a big puzzle in cognitive neuroscience. The task of recreating human vision from brain recordings, especially using non-invasive tools like functional Magnetic Resonance Imaging (fMRI), is an exciting but difficult task. Non-invasive methods, while less intrusive, capture limited information, susceptible to various interferences like noise. Furthermore, the acquisition of neuroimaging data is a complex, costly process. Despite these complexities, progress has been made, notably in learning valuable fMRI features with limited fMRI-annotation pairs.
#MinDVideo #fMRI
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