A lot of concept artists in the movie industry particularly in set/environment design and character design are getting extremely nervous. There will always be a need for a human to retouch and finalise a final asset but the sheer power of AI to turn around different previz designs so fast and so cheaply is fast becoming a very attractive tool. Why would you hire a person to show you potential designs when the AI will crunch 100s in a matter of hours for you to flip through and short list. Even designers will use that as a tool. The industry is going to shift very rapidly.
A Turing test is an interesting way of looking at this.
I think Marc may be right . I know some people who do story boarding and topical illustration to order. Interesting to hear what they think. Although the directors may be looking for more connected and human than than simply a nice image you would think AI capability could cut into this work at some level. The random and iterative nature of the production process however mitigates against this so it would simpler to pay an artist to provide what you ask for where the costs are relatively minor to the cost of the film.
Quote:
Originally Posted by gary
1 Sept 2022, vice.com
"An AI-Generated Artwork Won First Place at a State Fair Fine Arts Competition, and Artists Are Pissed"
The capabilities of producing images of faces that have never existed is astounding. A little trouble with ethnicity and geography with an indigenous surfer, which could cause ructions, but impressive.
Now you can buy an AI brain for your camera. Expect more nightscapes to have a similar look
Abstract
Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D assets and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited into any 3D environment. Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.
A neural radiance field (NeRF) is a fully-connected neural network that can generate novel views of complex 3D scenes, based on a partial set of 2D images.
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we scale up the system as a high definition text-to-video model including design decisions such as the choice of fully-convolutional temporal and spatial super-resolution models at certain resolutions, and the choice of the v-parameterization of diffusion models. In addition, we confirm and transfer findings from previous work on diffusion-based image generation to the video generation setting. Finally, we apply progressive distillation to our video models with classifier-free guidance for fast, high quality sampling. We find Imagen Video not only capable of generating videos of high fidelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos and text animations in various artistic styles and with 3D object understanding.
My conversation with an artificial intelligence system. Probably beat most people at pub trivia.
Me: "Tell me about Edwin Land's research on colour constancy"
Quote:
Originally Posted by ChatGPT
Edwin Land was an American scientist and inventor best known as the co-founder of the Polaroid Corporation. One of his notable contributions to the field of science was his research on color constancy, which explored the ability of the human visual system to perceive the colors of objects consistently under varying lighting conditions.
In the 1950s, Land and his colleagues at the Polaroid Corporation conducted a series of experiments to study the mechanisms behind color constancy. They found that the human visual system uses a combination of visual cues, such as the amount of light reflected by an object and the spectral distribution of the light source, to adjust the perception of colors in order to maintain a consistent perception of an object's color.
This research helped to improve the accuracy of color reproduction in photography and led to the development of new color film technologies that could better capture the colors of objects under different lighting conditions. It also laid the foundation for further research on the mechanisms of color perception in the human visual system.
Me: "and in what year did Polaroid introduce the SX-70?"
Quote:
Originally Posted by ChatGPT
The SX-70 is a type of instant camera developed by the Polaroid Corporation. It was first introduced in 1972. The SX-70 was notable for its sleek and compact design, as well as its ability to produce high-quality instant prints using a new type of film developed by Polaroid called SX-70 film.
The SX-70 was a popular camera, and it helped to establish Polaroid as a leader in the field of instant photography. It was also the first camera to use Polaroid's autofocus system, which automatically adjusts the focus of the camera based on the distance of the subject. This feature, combined with the camera's other advanced features, made it a popular choice for both amateur and professional photographers.
Me: What is the connection between Edwin Land and the U2 spy plane?
Quote:
Originally Posted by ChatGPT
Edwin Land, the co-founder of the Polaroid Corporation, was a scientific advisor to the United States government and worked on a number of classified projects, including the development of the U-2 spy plane.
The U-2 is a high-altitude reconnaissance aircraft used by the United States Air Force for surveillance and intelligence gathering. It was developed in the 1950s and was flown on secret missions over the Soviet Union and other hostile territories during the Cold War.
Land's work on the U-2 involved the development of new optical systems for the aircraft's cameras. He designed special lenses and filters that could capture high-resolution images from extreme altitudes, and he worked on techniques for improving the quality of the images produced by the cameras.
Overall, Land's contributions to the U-2 program helped to make the aircraft a valuable tool for gathering intelligence during the Cold War.
Me: "Can you write me a poem about Polaroid?"
Quote:
Originally Posted by ChatGPT
Sure, here is a possible poem about Polaroid:
In a world of digital screens,
Polaroid remains a dream,
A relic of a time gone by,
When pictures took time to develop.
With its instant film and flash,
Polaroid captured moments,
In a way that no device could match,
With its unique, one-of-a-kind prints.
No two photos were the same,
Each one a unique creation,
A true work of art,
That captured the essence of a moment.
But now, Polaroid is gone,
A victim of progress and change,
Leaving behind a legacy,
Of memories captured on film.
Though it may be gone,
Polaroid will never be forgotten,
For its photos will live on,
As a testament to its greatness.
I suppose it saves looking things up on Wikipedia when you can ask your watch.
And chargeable.
Very clever to comb the English speaking world and corner the market in information , although you would only have information with a particular cultural bias which might become obsolete quicker than it is updated.
Me: Could you write me a song about a guy at a star party whose pants catch on fire from the sun reflecting off the mirror of a big telescope?
Quote:
Originally Posted by ChatGPT
Yes, here is a song about a guy at a star party whose pants catch on fire from the sun reflecting off the mirror of a big telescope:
Verse 1:
He was just a guy at a star party
Gazing at the night sky
When all of a sudden, he heard a shout
And looked down to see his pants on fire
Chorus:
His pants were on fire, oh no
It was a sight to see
He was dancing around, trying to put it out
But the flames just kept on growing higher
Verse 2:
The cause of the fire was soon revealed
It was the sun reflecting off the mirror
Of the big telescope, pointing at the sky
It was a lesson to be careful, for sure
Chorus:
His pants were on fire, oh no
It was a sight to see
He was dancing around, trying to put it out
But the flames just kept on growing higher
Bridge:
But despite the mishap, he didn't give up
He kept on gazing at the stars
And when the fire was finally out
He just laughed it off and went back to observing
Chorus:
His pants were on fire, oh no
It was a sight to see
He was dancing around, trying to put it out
But the flames just kept on growing higher.
Fair point... not certain but I tend to think "cool"
A couple of my nieces looked at some examples and said they felt they
fell into the "uncanny valley".
Technically brilliant and with the right portrait - amazing.
But when it is not quite right, that feeling that the person is unnervingly strange in some way
and that you would probably avoid them.
I suspect the re-animation of someone you know well and are familiar with
every nuance of their face and facial expressions is more likely to slide
into the uncanny valley than someone who is unfamiliar to you.
The uncanny valley happens with some of the current text to image
systems as well when creating portraits. Synthesizing a face sets about
as high a bar as possible when it comes to accepting something for real
that was actually computer generated.
Deep Nostalgia
