Mind-Video is a tool built using the create-react-app that primarily deals with video-related applications. It is a JavaScript-based application, therefore it requires users to enable JavaScript on their web browsers to run smoothly.

Mind-Video incorporates several functionalities that enhance the user experience, offering a diverse range of services reliant on AI-based video analysis and processing.

Key features may include AI-driven video enhancement, automatic tagging, content recommendations, and enhanced search capabilities. It indicates a possible focus on improving accessibility and user engagement by using machine learning and AI techniques to handle, manage, and optimize video content.

Being built on the create-react-app framework, the app offers seamless setup, hot reloading, and overall improved productivity, resulting in a sound, efficient infrastructure for users.

Users should be aware that the capabilities of Mind-Video may vary and scale depending upon the continual advancements in AI technology. It's an excellent curated AI tool for individuals or organizations focusing on video-oriented projects and products.

Releases

Get notified when a new version of Mind Video is released

Notify me

Initial release

May 22, 2023

Initial release of Mind Video.

+ Submit new release

Pricing

Pricing model

No Pricing

Use tool

Save

🔗 Copy link

🗳️ Vote Best AI Tool

Featured

Thought to video Mind Video

Thought to video

6,606

No ratings

Overview Releases Pricing Reviews Prompts Pros & Cons Q&A Alternatives Recommendations

Use tool

Save

CodeRabbit

Enhanced code review for improved workflow and quality.

Code reviews

Open

847,945
637
3.9

v1.6 released 19d ago
Free + from $12/mo
Share

741,991 coderabbit.ai

Sahil Mohan Bansal

🙏 233 karma

Nov 13, 2024

Reducing manual efforts in first-pass during code-review process helps speed up the "final check" before merging PRs

15821 Reply Share Edit Delete Report
Averi AI

The AI Marketing Workspace: Strategy, Content, Team in One

Marketing

Open

12,511
95
4.3

v2.1 released 2mo ago
Free + from $45/mo
Share

Zach Chmael

🛠️ 1 tool 🙏 58 karma

Mar 10, 2025

This is the first AI marketing tool I’ve used that actually helps me get things done, not just suggest generic ideas. The mix of AI-powered strategy and real experts executing the work makes it feel like having a high-performance marketing team without the overhead

499 Reply Share Edit Delete Report

Pros and Cons

Pros

High-quality video generation

fMRI data utilization

Bridges image-video brain decoding gap

Spatiotemporal attention application

Augmented Stable Diffusion model

Trains encoder modules separately

Co-trains encoder and model

Two-module pipeline design

Flexible and adaptable structure

Progressive learning scheme

Accurate scene dynamics reconstruction

Multi-stage brain feature learning

Attains high semantic accuracy

Achieves 85% metric accuracy

Improved understandability of cognitive process

Demonstrates visual cortex dominance

Hierarchical encoder layer operation

Volume and time-frame preservation

Masked brain modelling application

Large-scale unsupervised learning approach

Multi-modal contrastive learning employed

Progressive semantic learning

Analytical attention analysis

Outperforms previous approaches by 45%

Reveals higher cognitive networks contribution

Encoder layers extract abstract features

Semantic metrics and SSIM evaluation

Stages of training show progression

Compression of fMRI time frames

Enhanced generation consistency

Guidance for video generation

fMRI encoder attention detail

Provides biologically plausible interpretation

Addresses hemodynamic response time lag

Incorporates network temporal inflation

Applicable to sliding windows

Integrates CLIP space training

Distills semantic-related features

Visually meaningful generated samples

Enhancement of semantic space understanding

Pipeline decoupled into two modules

Uses Human Connectome Project data

Analyzes layer-dependent hierarchy in encoding

Preserves scene dynamics within frame

Improvement through multiple training stages

Flexible and adaptable pipeline construction

Coding enables learning multiple features

Encoder focus evolves over time

View 43 more pros

Cons

Requires large-scale fMRI data

Dependant on quality of data

Complex two-module pipeline

Extensive training periods

Relies on annotated dataset

Requires fine-tuning processes

Transformer hierarchy can complicate processes

Semantics learning is gradual

Dependent on specific diffusion model

Focus on visual cortex not universally applicable

View 5 more cons

Q&A

What is the primary function of Mind-Video?

Mind-Video is an AI tool primarily designed to reconstruct high-quality videos from brain activity. This is achieved by capturing continuous functional magnetic resonance imaging (fMRI) data.

How does Mind-Video reconstruct video from brain fMRI data?

Mind-Video uses a two-module pipeline to reconstruct videos from brain fMRI data. The first module focuses on learning general visual fMRI features through unsupervised learning with masked brain modeling and spatiotemporal attention. It follows this by distilling semantic-related features through multimodal contrastive learning with an annotated dataset. The second module fine-tunes these learned features using co-training with an augmented stable diffusion model that is specifically designed for video generation guided by fMRI data.

What sets Mind-Video apart from previous fMRI-Image reconstruction tools?

Mind-Video stands apart from previous fMRI-Image reconstruction tools because of its ability to recover continuous visual experiences in video form from non-invasive brain recordings. Its flexible and adaptable two-module pipeline consists of an fMRI encoder and an augmented stable diffusion model that are trained separately and finetuned together. Its progressive learning scheme allows the encoder to learn brain features in multiple stages, resulting in high semantic accuracy videos that outperform previous state-of-the-art approaches.

Can you describe the two-module pipeline in Mind-Video?

Mind-Video's two-module pipeline starts with the first module, which concentrates on learning general visual fMRI features via unsupervised learning with masked brain modeling and spatiotemporal attention. This module distills semantic-related features using multimodal contrastive learning with an annotated dataset. Then, the second module fine-tunes these learned features by co-training with an augmented stable diffusion model that is specifically tailored for video generation under fMRI guidance.

How are the semantic-related features distilled in Mind-Video?

In Mind-Video, the semantic-related features are distilled using the multimodality of the annotated dataset. This stage involves training the fMRI encoder in the CLIP space with contrastive learning.

What role does the Stable Diffusion model play in Mind-Video?

The Stable Diffusion model in Mind-Video plays a crucial role in guiding the video generation. Following the learning of general and semantic-related features from the fMRI data in the first module, the second module fine-tunes these features by co-training with an augmented stable diffusion model. This process specifically focuses on guiding the generation of videos under the influence of fMRI data.

+ Show 13 more

What change in learning is observed in the fMRI encoder throughout its training stages?

Throughout its training stages, the fMRI encoder in Mind-Video shows progressive improvement in assimilating nuanced semantic information. The encoder learns brain features in multiple stages and shows an increased attention to higher cognitive networks and decreased focus on the visual cortex over time, demonstrating its progressive learning ability.

What were the results when Mind-Video was compared with state-of-the-art approaches?

When compared with state-of-the-art approaches, Mind-Video demonstrated superior results. It achieved an accuracy of 85% in semantic metrics and 0.19 in SSIM, a measure of the structural similarity between the reconstructed video and the original, outperforming the previous best approaches by 45%.

What areas of the brain were found to be dominant in processing visual spatiotemporal information?

The attention analysis of the transformers decoding fMRI data in Mind-Video showed a dominance of the visual cortex in processing visual spatiotemporal information. However, higher cognitive networks, such as the dorsal attention network and the default mode network, were also found to contribute to the visual perception process.

How does Mind-Video ensure generation consistency in its process?

Mind-Video ensures generation consistency in its process by meticulously preserving the dynamics of the scene within one fMRI frame while enhancing the generation consistency. This equilibrium is critical for accurate and stable reconstruction over one fMRI time frame.

Why does Mind-Video utilize data from the Human Connectome Project?

Mind-Video utilizes data from the Human Connectome Project as it provides large-scale fMRI data. This comprehensive set of brain imaging data aids in the effective analysis, learning, and reconstruction of visual experiences from brain recordings.

Who are the main contributors and supporters in the development of Mind-Video?

The contributors to the development of Mind-Video include Zijiao Chen, Jiaxin Qing, and Helen Zhou from the National University of Singapore and the Chinese University of Hong Kong as well as collaborators from the Centre for Sleep and Cognition and the Centre for Translational Magnetic Resonance Research. The tool also acknowledges supporters such as the Human Connectome Project, Prof. Zhongming Liu, Dr. Haiguang Wen, the Stable Diffusion team, and the Tune-a-Video team.

What is the primary motivation and research gap Mind-Video aims to address?

Mind-Video aims to address the challenge of recovering continuous visual experiences in video form from non-invasive brain recordings. This was the primary motivation for its development. The research gap it aims to fill involves overcoming the time lag in the hemodynamic response for processing dynamic neural activities and enhancing the generation consistency while ensuring the dynamics of the scene within one fMRI frame are preserved.

What makes Mind-Video's brain decoding pipeline flexible and adaptable?

Mind-Video's brain decoding pipeline is made flexible and adaptable through its decoupling into two modules. These are the fMRI encoder and the augmented stable diffusion model, which are trained separately and then fine-tuned together. This design allows the encoder to progressively learn brain features through multiple stages, resulting in a flexible and adaptable pipeline.

How did Mind-Video achieve high semantic accuracy?

Mind-Video achieves high semantic accuracy through a comprehensive learning and fine-tuning process. The encoder learns brain features in multiple stages, building from general visual fMRI features to more semantic-related characteristics. The augmented stable diffusion model then fine-tunes these features, guided by the fMRI data. This results in a recovered video with high semantic accuracy, including motions and scene dynamics.

What is the role of the multimodal contrastive learning in Mind-Video?

The multimodal contrastive learning in Mind-Video serves to distill semantic-related features from the general visual fMRI features learned via unsupervised learning. It utilizes the multimodality of the annotated dataset, training the fMRI encoder in the CLIP space to focus on these essential semantics.

What insights were gained from the attention analysis of the transformers decoding fMRI data in Mind-Video?

The attention analysis of the transformers decoding fMRI data in Mind-Video reveals that the visual cortex is dominant in processing visual spatiotemporal information. It also shows a hierarchical nature of the encoder's layers in extracting visual features—initial layers focus on structural information, while deeper layers shift towards learning more abstract visual features. Finally, the fMRI encoder demonstrates progressive improvement in assimilating more nuanced, semantic information throughout its training stages.

How can I access the code for Mind-Video?

The code for Mind-Video can be accessed via [this GitHub repository](https://github.com/jqin4749/MindVideo).

Can Mind-Video's pipeline be fine-tuned according to my needs?

Yes, the two-module pipeline that forms the core of Mind-Video—consisting of an fMRI encoder and an augmented stable diffusion model— is designed to be flexible and adaptable for fine-tuning according to specific needs. They are trained separately and can be fine-tuned together, offering a high degree of customization.

Ask a question

Submit

If you liked Mind Video

Featured matches

VideoIdeas.ai

Generate viral YouTube scripts and ideas in minutes.

Youtube scripts

Open

27,239
89
3.7

Released 6mo ago
Free + from $8/mo
Share

21,300 www.videoideas.ai

Aleric Heck

🛠️ 3 tools 🙏 230 karma

Mar 10, 2025

VideoIdeas.ai Creates YouTube Video Ideas & Scripts

317 Reply Share Edit Delete Report
V03 AI Video Generator

Instantly create AI videos with audio using Google Veo 3

Videos

Open

8,397
21
2.7

Released 2mo ago
From $9.99/mo
Share

james

🛠️ 3 tools 🙏 78 karma

Jul 17, 2025

v03 ai is the only veo 3 videos generator supporting vertical videos for instagram, tiktok and other social media platforms, try it now!

348 Reply Share Edit Delete Report

Verified tools

VideoTube

Generate AI videos from text, image, and video in minutes.

Videos

Open

10,089
60
3.7

Released 5mo ago
From $9.9/mo
Share
939 videotube.ai

sum sum

🙏 30 karma

Apr 21, 2025

One platform gives me access to various high-quality video models — it’s been used to generate 110k viral social media posts.

204 Reply Share Edit Delete Report

Search

Mind Video

Releases

Pricing

Related tasks

Reviews

How would you rate Mind Video?

Prompts & Results

Pros and Cons