AI Technology & Industry Review
A research team from Leibniz University Hannover introduces World-GAN, a 3D generative adversarial network that aims to learn and generate structures directly in the 3D voxel space in Minecraft.
The approach dramatically reduces bandwidth requirements by sending only a keypoint representation [of faces] and reconstructing the source video on the receiver side with the help of generative adversarial networks (GANs) to synthesize the talking heads.
A Princeton student designed a GAN framework for Chinese landscape painting generation that is so effective most humans can’t distinguish its works from the real thing.
Pinkney and Adler NeurIPS 2020 workshop paper enables realistic image generation in domains such as animation and ukiyo-e with creative control on the output.
Novel model uses a quality estimator and evolutionary optimization to search the latent space of GANs trained on limited datasets.
Imaginaire, a universal PyTorch library designed for various GAN-based tasks and methods.
NVIDIA researchers propose a novel vid2vid framework that utilizes all past generated frames during rendering.
Do you have two left feet? Do you avoid the dance floor out of fear of embarrassment? If you’ve ever secretly wished you could move your body like Joaquín Cortés — well, at least in a video — a new AI-powered 3D body mesh recovery module called Liquid Warping GAN can give you a leg up.
In the paper ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer, a team of researchers from Peking University and Tsinghua University propose an end-to-end GAN-based architecture that can transfer input photos into the style of Chinese ink wash paintings.
“Adversaries and strategic competitors probably will attempt to use deep fakes or similar machine-learning technologies to create convincing — but false — image, audio, and video files to augment influence campaigns directed against the United States and our allies and partners.”
A team of researchers from the Norwegian University of Science and Technology recently proposed a new architecture that can anonymize faces in images automatically while the original data distribution remains uninterrupted.
A new research paper from Berlin-based unicorn fashion and technology company Zalando uses generative adversarial networks (GANs) to produce high-resolution images of virtual fashion models ready to model clothes of any style.
Ian Goodfellow — the research scientist who pioneered generative adversarial networks (GANs) — has left Google Brain and joined Apple to direct a special machine learning project, according to his Linkedln profile updated today and CNBC.
Andrew Brock, first author of the high-profile research paper Large Scale GAN Training for High Fidelity Natural Image Synthesis (aka “BigGAN”), has posted a GitHub repository of an unofficial PyTorch BigGAN implementation that requires only 4-8 GPUs to train the model.
Synced Global AI Weekly March 24th
At the NVIDIA GPU Technology Conference (GTC) which kicked off today, NVIDIA unveiled its latest image processing research effort — GauGAN, a generative adversarial network-based technique capable of transforming segmentation maps into realistic photos.
GTC 2019 runs next Monday through Thursday (March 18 — 21), and while we can only speculate what surprises NVIDIA CEO Jensen Huang might have in store for us, we can get some sense of where the company is headed by looking at what it’s been up to for the last 12 months.
As Synced previously reported, these hyperrealistic images now flooding the Internet come from US chip giant NVIDIA’s StyleGAN, a generative adversarial network based face generator that performs so well that most people can’t distinguish its creations from photos of real people.
10 AI News You Must Know from February W 1 – W 2
In December Synced reported on a hyperrealistic face generator developed by US chip giant NVIDIA. The GAN-based model performs so well that most people can’t distinguish the faces it generates from real photos. This week NVIDIA announced that it is open-sourcing the nifty tool, which it has dubbed “StyleGAN”.
Many scientists believe that a quantum computing device will one day perform highly complex computational tasks which are far beyond the ability of today’s classical computers.
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The number of AI-related research papers has skyrocketed in recent years, outpacing papers from all other academic topics since 2000. This has, not unsurprisingly, resulted in a shortage of qualified peer reviewers in the machine learning community, particularly when it comes to conference paper submissions.
Text-based CAPTCHA remain one of the most visible and commonly used mechanisms for website security. As a sort of online gatekeeper that distinguishes between humans and bots, the little solvable image fields have critical commercial applications in blocking automatic spam and preventing e-transfer fraud; and can also stop bots from spreading fraudulent information, etc.
The digital painting tool GANpaint has gone viral on social media. The product of a team of high-profile researchers from MIT, IBM, Google, and the Chinese University of Hong Kong, GAPpaint allows anyone — even those with little knowledge of digital painting or photoshop — to “paint” incredibly complex and detailed photorealistic scenes.
Founded in 1999, Tokyo-based DeNA has developed popular platforms and services for gaming, E-commerce, automotive, healthcare and entertainment content distribution. As AI continues transforming all things digital, DeNA is expanding its deep learning tech capabilities to support R&D on new techniques.
Georgia Tech and Google Brain researchers have introduced the new interactive tool GAN Lab, which visually presents the training process of complex machine learning model Generative Adversarial Networks (GANs). Even machine learning newbs can now experiment with GAN models using only a common web browser.
In a new paper Durham University researchers introduce a anomaly detection model, GANomaly, comprising a conditional generative adversarial network that “jointly learns the generation of high-dimensional image space and the inference of latent space.” The process enables the model to perform anomaly detection tasks even in sample-poor environments.
Compositing multiple objects from different sources into one image is a formidable computer graphics challenge involving relative scaling, spatial layout, occlusion, and viewpoint transformation of objects from the 3D world to a 2D image.
Electrifying an entire dance club is easy if you have killer moves like John Travolta in Saturday Night Fever. But for the rest of us, not so much. We may shake our butts and swing our arms, but let’s face it: some people just can’t dance. But now there’s hope, thanks to AI.
A Google Brain research group has published a paper proposing a new method for evaluating generative models by using insights from competitive games between human players.
YouTuber “TJWei” recently uploaded a “Face-off” video on YouTube. The four-minute split-screen clip sees two figures speaking and emoting simultaneously. Both look human, but one is an entity generated in real-time by an AI algorithm.
CVPR 2017 conference covered topics in: Machine Learning, Object Recognition & Scene Understanding – Computer Vision & Language, 3D Vision, Human Analyzing, Low- & Mid- Level Vision, Image Motion & Tracking: Video Analysis, Computational Photography, Applications.
PixelGAN is an autoencoder for which the generative path is a convolutional autoregressive neural network on pixels, conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.