Instant NeRF is a neural rendering model that learns a high-resolution 3D scene in seconds — and can render images of that scene in a few milliseconds.
When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds.
Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly — making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering. Read More
Tag Archives: Image Recognition
Impressive photo restoration by AI !
This new and completely free AI model can fix most of your old pictures in a split second!
Do you also have old pictures of yourself or close ones that didn’t age well or that you, or your parents, took before we could produce high-quality images? I do, and I felt like those memories were damaged forever. Boy, was I wrong!
This new and completely free AI model can fix most of your old pictures in a split second. It works well even with very low or high-quality inputs, which is typically quite the challenge.
This week’s paper called Towards Real-World Blind Face Restoration with Generative Facial Prior tackles the photo restoration task with outstanding results. What’s even cooler is that you can try it yourself and in your preferred way. They have open-sourced their code, created a demo and online applications for you to try right now. If the results you’ve seen above aren’t convincing enough, just watch the video and let me know what you think in the comments, I know it will blow your mind! Read More
PeopleLens: Using AI to support social interaction between children who are blind and their peers
CSLIM Brings AI-Generated Art to ‘The World,’ or Vice Versa
Welcome to the world of tomorrow, where artificial intelligence can create East Asian landscape paintings that rival 11th century masters’ — with ownership that can be verified on the blockchain. Such is the vision of South Korean generative artist CSLIM, whose upcoming NFT collection will feature 5,000 artworks that the engineer and developer attests were created by AI that has studied the data of 80,000 classical East Asian landscape paintings from the sixth through 13th centuries via machine learning. Exquisitely combining cultural tradition and cutting-edge technology, “The World” will drop on Feb. 21 — only at Crypto.com/NFT. Read More
I asked an AI to paint 10 famous sci-fi book titles in one minute. Here are the results.
Fifty years ago, top scientists believed AI would never be able to beat humans at chess.
We all know how that turned out.
The newest goalpost is art. As an AI artist, people routinely tell me how my art isn’t real, or how it lacks humanity because it’s machine generated.
True, there’s some credence to these claims. But I would venture most people are simply uncomfortable with the notion that AI is starting to produce art faster & better than humans.
After my last post on AI art blew up, it naturally attracted a fair amount of similar criticism. Much was in reference to a supposed lack of diversity in the samples (apparently all of the landscapes looked the same).
To respond to this criticism, I decided to run an A100 GPU for one minute. Read More
People Trust Deepfake Faces Generated by AI More Than Real Ones, Study Finds
The proliferation of deepfake technology is raising concerns that AI could start to warp our sense of shared reality. New research suggests AI-synthesized faces don’t simply dupe us into thinking they’re real people, we actually trust them more than our fellow humans.
In 2018, Nvidia wowed the world with an AI that could churn out ultra-realistic photos of people that don’t exist. Its researchers relied on a type of algorithm known as a generative adversarial network (GAN), which pits two neural networks against each other, one trying to spot fakes and the other trying to generate more convincing ones. Given enough time, GANS can generate remarkably good counterfeits.
Since then, capabilities have improved considerably, with some worrying implications: enabling scammers to trick people, making it possible to splice people into porn movies without their consent, and undermining trust in online media. While it’s possible to use AI itself to spot deepfakes, tech companies’ failures to effectively moderate much less complicated material suggests this won’t be a silver bullet. Read More
FILM: Frame Interpolation for Large Scene Motion
Tensorflow 2 implementation of our high quality frame interpolation neural network. We present a unified single-network approach that doesn’t use additional pre-trained networks, like optical flow or depth, and yet achieve state-of-the-art results. We use a multi-scale feature extractor that shares the same convolution weights across the scales. Our model is trainable from frame triplets alone. Read More
Fake It Till You Make It
We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone.
The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human faces. Researchers have tried to bridge this gap with data mixing, domain adaptation, and domain-adversarial training, but we show that it is possible to synthesize data with minimal domain gap, so that models trained on synthetic data generalize to real in-the-wild datasets.
We describe how to combine a procedurally-generated parametric 3D face model with a comprehensive library of hand-crafted assets to render training images with unprecedented realism and diversity. We train machine learning systems for face-related tasks such as landmark localization and face parsing, showing that synthetic data can both match real data in accuracy as well as open up new approaches where manual labelling would be impossible. Read More
Dataset
Corsight’s Upcoming DNA to FACE: ‘Terrifying’ Warns Privacy Expert
Corsight plans to release a new product that combines DNA and face recognition technology and could have significant law enforcement and privacy implications.
In this report, we examine Corsight’s product roadmap for “DNA to FACE,” presented at the 2021 Imperial Capital Investors Conference, possible use cases for the technology, and warnings from a privacy expert.
IPVM collaborated with MIT Technology Review on this report, see the MIT Technology Review article: This company says it’s developing a system that can recognize your face from just your DNA Read More
TransformerFusion
Monocular RGB Scene Reconstruction using Transformers
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing the scene; this feature grid is then decoded into an implicit 3D scene representation. Key to our approach is the transformer architecture that enables the network to learn to attend to the most relevant image frames for each 3D location in the scene, supervised only by the scene reconstruction task. Features are fused in a coarse-to-fine fashion, storing fine-level features only where needed, requiring lower memory storage and enabling fusion at interactive rates. The feature grid is then decoded to a higher-resolution scene reconstruction, using an MLP-based surface occupancy prediction from interpolated coarse-to-fine 3D features. Our approach results in an accurate surface reconstruction, outperforming state-of-the-art multi-view stereo depth estimation methods, fully-convolutional 3D reconstruction approaches, and approaches using LSTM- or GRU-based recurrent networks for video sequence fusion. Read More
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