Video/Image

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Video Synthesis



Text-to-Video

Image-to-Video

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Deep Nostalgia™ is a one-of-a-kind feature that allows you to animate the faces in your old family photos. Utilizing state-of-the-art deep learning technology licensed by MyHeritage from D-ID, Deep Nostalgia™ creates high-quality, realistic video footage from still photos.

"The draw here is that visual imagery is visceral and compelling and we respond to it," says Hany Farid, associate dean and head of the School of Information at UC Berkeley. "We are visual beings. When you see your grandmother or Mark Twain come alive, there's something fascinating about it." A new program can animate old photos. But there's nothing human about artificial intelligence | AJ Willingham - CNN

Video-to-Video

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Video Editing

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In the last few years, artificial intelligence (AI) and machine learning (ML) have both started to feature more prominently in technology. That is especially the case in video editing, where artificial intelligence is being integrated with more and more ways. How Artificial Intelligence is Transforming Video Editing | TechnologyHQ


Captioning

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Image Captioning Through Neural Networks
An image captioning model using VGG16 feature extraction (CNN) and LSTM (RNN) neural networks. With Python, [TensorFlow]] and Keras

Automated Image Captioning with ConvNets and Recurrent Nets
Andrej Karpathy and Fei-Fei Li ...http://cs.stanford.edu/people/karpathy/sfmltalk.pdf

A Simple Way to Automatically Transcribe Video/Audio to Text
Here is how to auto generate subtitles from any video with Google docs. It also works if you want to convert audio to text. Useful for creating subtitles and closed Captions for all your Youtube videos. The trick here is to make the recording and playback devices same. This will give good quality and help you automatically transcribe video to text.

Generating Captions from Images with Deep Learning
Ronjon Nag speaks for first part about the history of neural networks in industry. Dave Sullivan speaks for the second part and explains how to use neural networks to generate captions from images

Level Up - Automated Subtitles with AI
Welcome to Level Up! the show where we show you how to build solutions hands-on with Google Cloud Platform. In this episode, Solutions Architect Markku Lepistö will show you how to create subtitles for videos using Cloud AI services. And then how to translate the subtitles to quickly add support for multiple languages!

00:35 - Example of extracting the dialog as an audio track 01:15 - Enable Google Cloud AI services 02:37 - Coding the client app in Python 09:20 - Reviewing the results 09:48 - Uploading subtitles to YouTube Studio 10:44 - Testing the results

AI-Driven Image Captioning For Inclusive Productivity
Advances in hybrid intelligence, deep learning, and related artificial intelligence techniques have provided us with a remarkable opportunity to ensure the future of work will be even more inclusive to more people than ever before. Because the communication and products of work increasingly comprise images—photos, charts, maps, and the like—that are often not accessible, people who are blind or low vision face unique challenges. One promising technology is the automated understanding and captioning of images. Microsoft Office 365 applications, for example, can use APIs from Microsoft Azure Cognitive Services to automatically add alt text to images. But there remain many hurdles to making these captions truly useful and usable. In this breakout session, we will explore the state of the art and potential for advancement in automated image captioning, including data capture and curation for training, caption presentation and interactivity, and computer vision.

Microsoft AI breakthrough in automatic image captioning
Microsoft researchers have built an artificial intelligence system that can generate captions for images that are, in many cases, more accurate than what was previously possible. The breakthrough is a milestone in Microsoft’s push to make its products and services inclusive and accessible to all users.

Pytorch Image Captioning Tutorial
In this tutorial we go through how an image captioning system works and implement one from scratch. Specifically we're looking at the caption dataset Flickr8k. There are multiple ways to improve the model: train a larger model (the one used is relatively small), train for longer and improve the model by adding attention similar to this paper: http://arxiv.org/abs/1502.03044. Video of dataset (link in that video description to download the dataset yourself): http://youtu.be/9sHcLvVXsns Support My Channel Through Patreon: http://www.patreon.com/aladdinpersson



Style-Based Generator Architecture

Stable Diffusion

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A latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Designing and implementing solutions using collective intelligence and augmented technology.

DALL-E

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DALL·E is a AI system that can create realistic images and art from a description in natural language. We currently support the ability, given a prommpt, to create a new image with a certain size, edit an existing image, or create variations of a user provided image.

The current DALL·E model available through our API is the 2nd iteration of DALL·E with more realistic, accurate, and 4x greater resolution images than the original model. You can try it through the our Labs interface or via the API.


MidJourney

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Image GPT

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NVIDIA Canvas

From Text

Tool-related

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Image or Video Forgeries

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I built a Secret AI Youtube Channel

I challenged myself to earn $100 in the first 3 days of starting a secret AI generated Youtube channel! I didn't use any of my existing connections to promote the channel and used AI tools like ChatGPT, MidJourney, and D-ID every step of the way to automate my own job as a Machine Learning Educator. I've open sourced all the code i used because I want more people to do this, AI avatars are going to be on social media alongside humans, building followers and influencing society. Was I successful in my goal? Find out in this video!

SciFi AI Movies


Generative Adversarial Network (GAN) employs two dueling neural networks to train a computer to learn the nature of a data set well enough to generate convincing fakes. When applied to images, this provides a way to generate often highly realistic fakery. In the most recent work, the researchers took inspiration from a technique known as Style Transfer to built their GAN in a fundamentally different way. This allowed their algorithm to identify different elements of a face, which the researchers could then control. These incredibly realistic fake faces show how algorithms can now mess with us | Will Knight - MIT Technology Review


To rework a famous saying, a fake picture is worth a thousand fake words, and with the increasing democratization of this kind of technology its going to become harder and harder to trust what we see on the web. As Joshua Rothman notes in the New Yorker, that presents a double-edge sword—not only will people be able to create forgeries to twist the public discourse, public figures will also have plausible deniability for anything they’re caught doing on camera. Nvidia’s Fake Faces Are a Masterpiece—But Have Deeper Implications | Edd Gent - SingularityHub

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