NVIDIA’s New AI: 50x Smaller Virtual Worlds!

Summary

This video explores cutting-edge AI techniques for generating virtual worlds and manipulating images and video. It highlights papers enabling high-quality compact virtual worlds, sculpting objects in images, directing image motion, and synthesizing video from audio. While imperfect now, the speaker invokes the 'First Law of Papers' - looking ahead just two more iterations, these methods could enable incredibly realistic and controllable synthetic media. Despite challenges, the rapid progress showcases human ingenuity and AI's vast potential.

Takeaways

• 😲 New NVIDIA technique creates highly detailed virtual worlds in a very compact size
• 😮 Intel & NYU method converts people in images to 3D models that can be posed and manipulated
• 🎥 New approach creates video from images by providing direction arrows
• 👀 AI understands how to animate a horse from scratch based on direction arrows
• 🌄 Just 2 more advancements could massively improve auto-generated video quality
• 🌌 Future virtual worlds may look incredibly realistic and detailed
• 🖼️ AI technique sculpt 3D models directly out of images
• 🍒 Consistency between auto-generated images is not yet perfect
• 🎞️ New method auto-generates virtual character movements from audio conversations
• 😕 Accurately animating subtle human expressions is extremely difficult

Q & A

What new NVIDIA technique is mentioned in the script and what are its benefits?

The script mentions a new NVIDIA technique for creating virtual worlds using neural radiance fields (NeRFs). Its main benefit is that it can achieve the same quality results in a much smaller size compared to previous methods.

What does the Intel and NYU collaboration allow us to do with images?

Their collaboration allows converting people and objects in images into 3D models. This enables changing their pose, rotating them, deforming them, and placing them into new images.

How does the technique for artistic direction of images work?

It involves adding arrows to indicate desired motions. The AI then synthesizes a video with the objects moving according to the arrows. It works well for camera motions and basic object motions.

What are some limitations of the AI-generated conversational characters?

The movements can seem stiff at times. The mouth movements are not very accurate yet. Overall it is impressive but still has room for improvement in capturing natural human expressions and gestures.

What is the First Law of Papers mentioned in the script?

It says research is a process - don't look at current capabilities but at capabilities two papers down the line. Referring to fast progress being made.

What are some examples that illustrate the rapid progress in AI?

The script mentions DALL-E 1 vs 2 for text to image generation as an example. Each iteration brings major improvements in quality and capabilities.

What makes synthesizing natural human interactions difficult?

Humans are wired to detect even subtle irregularities in expressions and behavior. Any slight unnaturalness is quickly noticed. Capturing the nuances is very challenging.

How does the technique for generating videos from audio work?

It analyzes videos of real conversations to learn patterns. Then given just audio input, it synthesizes virtual characters including mouth and gestures to have a conversation.

What does the script say about the future potential for video synthesis AI?

It says given the rapid progress, in just a couple more iterations these techniques could reach a 'DALL-E 2 moment' with significantly improved quality and capabilities.

What are some key themes and ideas conveyed in the script?

The pace of progress in AI, generating synthetic media, importance of naturalness and consistency, seeing the bigger picture beyond current limitations.

Outlines

00:00

😲Revolutionizing virtual worlds with new techniques

This paragraph discusses several new techniques that significantly improve the quality and efficiency of generating virtual worlds from images. It covers NVIDIA's model, Instant Neural Graphics, Gaussian Splatting, and an unnamed new technique that achieves even better compression. The core ideas are leveraging neural representations, convergence speed, balancing quality and size, and the potential for future improvements.

05:03

😃Sculpting and modifying images with AI

This paragraph showcases techniques for converting people and objects in images into editable 3D models. This allows modifying their pose, position, count, and applying deformations while realistically integrating them back into the image. Some limitations around artifacts are noted, but the potential is highlighted, invoking the First Law of Papers regarding future progress.

🎥AI-directed video synthesis from images

This paragraph demonstrates AI-generated video from static images based on directional arrows. It works for camera movement and surprisingly synthesizes credible horse motion as well. The challenges around perfect synthesis and the enormous future potential after expected quick progress are discussed.

😀Animating conversational virtual characters

This paragraph presents an AI method for automatically animating virtual character conversations from audio alone, with gestures and expressions. The synthesized motions can be expressive but have issues with accuracy and stiffness. The difficulty of perfect replication and the potential solution of future AI progress are examined.

Mindmap

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Keywords

💡NERF

NERF stands for Neural Radiance Fields. It is a 3D representation technique that uses neural networks to synthesize novel views of scenes and objects from collections of images. The video discusses different NERF techniques for creating virtual worlds more efficiently. For example, Instant Neural Graphics can generate high quality NERF models much faster.

💡gaussian splatting

Gaussian splatting is a technique to create animatable NERF models of dynamic scenes. It is mentioned as an impressive predecessor to the new NERF method discussed, which achieves even higher compactness and efficiency.

💡3D model

A 3D model is a mathematical representation of a 3D object or scene. The video talks about techniques to take a 2D image and convert parts of it into a 3D model. This allows manipulating the image by reposing, rotating or deforming the 3D modeled parts.

💡artistic direction

Artistic direction refers to visually directing the desired motion in an image or video. One technique discussed takes an image and arrow annotations indicating intended motion, and generates a video with that motion.

💡video synthesis

Video synthesis means artificially generating video content. The video discusses techniques to synthesize realistic video by providing only sparse input like audio or coarse motion arrows. This has applications like easily producing animations.

💡facial animation

Facial animation involves modeling and animating lifelike facial expressions and speech. One technique analyzed videos of conversations to learn to generate realistic mouth shapes and gestures from just audio input.

💡virtual character

A virtual character is a simulated human or humanoid agent in a virtual environment. The video describes a technique to automatically generate animated virtual characters having a conversation based on provided audio only.

💡virtual world

A virtual world is a simulated 3D environment modeled using computer graphics. Several techniques are presented to efficiently construct high quality virtual worlds from image data.

💡image editing

Image editing means modifying existing images, like retouching or compositing visual elements. The discussed techniques enable complex edits like reshaping scene elements or realistically inserting new objects.

💡generative models

Generative models can synthesize new content like images, video or 3D models based on learning from training data. The paper uses them to generate high quality novel views and animations from limited input.

Highlights

New NVIDIA technique has same quality but is 5x smaller in size vs. prior work.

New technique packs quality of Instant Neural Graphics in 1/5th the size.

New technique is 50x more compact than legendary Gaussian Splatting.

Sculpting images by converting people/objects into 3D models.

Applying artistic direction to images by indicating desired motion.

AI understands how a horse should move and synthesizes it from scratch.

AI technique creates virtual characters, mouth movements, and gestures from just audio input.

Synthesized movements are often expressive but sometimes stiff and inaccurate.

Our brains immediately notice even slight inaccuracies in facial expressions.

Making realistic virtual conversations will be incredibly difficult.

Ingenuity and AI power may eventually enable realistic virtual conversations.

Look at progress over time, not just current limitations.

Future work could enable incredible control over image manipulation.

AI techniques make creating virtual worlds much more accessible.

Rapid advances in AI will enable next level synthesized video.

Transcripts

Today we are going to create absolutely incredible  virtual worlds with these new papers. First,  

NVIDIA did something here, but if the quality  does not seem to be too much better here,  

then how does this really  help? We’ll find out together.

Then, we are going to re-sculpt an image  

with this collaboration between  Intel and New York University.

Then, we will become a movie  director and give directions to,  

not people, but get this: images. Oh yes.

And then, we won’t even need  to direct these images. This  

AI technique directs the video by itself.

Dear Fellow Scholars, this is Two Minute  Papers with Dr. Károly Zsolnai-Fehér.

So, with NERFs, we are able to gather a bunch of  photos, and have a technique stitch it together  

into a virtual world. There are models that  can do this extremely quickly, for instance,  

Instant Neural Graphics. This converges in a  matter of seconds, which is kind of insane. 

And its quality is often even better than its  predecessors. You see a great deal more detail  

in the hair and the sweater. And now, let’s  see the new technique! Ready to be blown away.  

Wait a second… this looks nearly the same!  So is this better? If it is better, how?

Well, what you see here is quality, but quality  is just half of the story! The other half is  

size. We haven’t talked about that yet.  The first technique is reasonably sized,  

but the quality is lacking, then comes  Instant Neural Graphics, quality much better,  

but the size is much larger. And now, look at  the new technique, which looks roughly the same,  

but, oh my, it packs the same quality but in one  fifth the size. Fantastic. In this sense, this is  

even better than the legendary new technique,  Gaussian Splatting, which can create and now,  

even animate virtual worlds, and this new one  is 50 times more compact than that. Crazy.

Now, let’s sculpt some images. Second paper.  Here, the goal is to take an image, any image,  

and then, convert the people or objects in  it into a 3D model, but not to create a video  

game character from them, although that’s quite  nice too, but no, not here! Here, we now have  

knowledge about the backside of this model too,  so we can choose a new pose for our character,  

and apply some more magic to put it back into the  image with the new pose. We can even rotate them,  

you name it. Shifting these objects to  new positions is also possible. And wait,  

these are 3D models, so we can even apply  deformations to them. Carve out that bad boy,  

and there we go! Apart from some  suspect artifacts at the mouth region,  

this one is almost perfect. Or placing  new ducklings or fish into an image?  

Not a problem. And this concept gives us a great  deal of control over these images. For instance,  

how many cherries would you like? How about  this one? And another one? And another one? The  

consistency between the images is not perfect, but  they are nearly the same. And just imagine what  

we will be capable of just two more papers down  the line. My goodness. What a time to be alive!

And, we are not done with magic for today,  not even close. With this other work,  

we can apply some more artistic direction to  already existing images. Just look at the arrows,  

this indicates our wishes as to how the image  should be moving, and bam! We get a video. This  

works great for camera movement, but you know  what. I wonder what happens if I instruct this  

horse to move. That is so much more complex than  just camera movement. So, what happens then? Now  

hold on to your papers Fellow Scholars, and…my  goodness. Look at that. The AI understands how a  

horse should move, and synthesizes exactly that.  It is not perfect, not even close, but this is  

once again an excellent opportunity to invoke  the First Law of Papers. What is that? Well,  

the First Law of Papers says that research  is a process. Do not look at where we are,  

look at where we will be two more papers down  the line. Remember what DALL-E 1 could do in  

terms of text to image, and then, DALL-E 2  dropped and blew it out of the water. Just  

imagine what a DALL-E 2 moment for this  kind of video synthesis could be. Wow.

And now, check this out. Here, this AI technique  looked at videos of people in real conversations,  

and then, all we need is our audio input.  Then, get this, it creates virtual characters,  

mouth movements and even gestures automatically  so we can have conversations in virtual worlds  

more easily. I have to say the synthesized  movements are often expressive, I give you  

that, but also sometimes a little stiff,  mouth movement is not that accurate yet,  

but it is very impressive that from just the  audio, all this can be synthesized. Once again,  

just two more papers down the line, and you might  start seeing this out there in the real world.

I think this work is a really good showcase of how  difficult this problem is. You see, our brains are  

wired to look at each other, and read each other’s  expressions. Thus, if even a little hesitation,  

just a tiny smirk, if just the slightest things  are off, we immediately know that something is  

wrong. We are wired for that. So making this  work properly will be incredibly difficult,  

but if something, human ingenuity and  the power of AI will be able to do that.