AI Technology & Industry Review
In a new paper, researchers from Max Planck Institute for Informatics and Facebook Reality Labs propose an end-to-end trainable method that enables re-rendering of humans from one single image.
As part of our year-end series, Synced highlights 10 AI-powered art projects that inspired and entertained us in 2020.
CSAIL researchers propose a framework for image reconstruction tasks using the state-of-the-art generative model StyleGAN2.
The new AI-powered Multi-Ingredient Pizza Generator (MPG) can deliver all these mouth-watering pies and many more.
NVIDIA blog introduced company’s latest NeurIPS presentation: applying a novel neural network training technique, adaptive discriminator augmentation, to the popular NVIDIA StyleGAN2 model.
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.
ICLR 2021 submitted paper proposes efficient VAEs that outperform PixelCNN-based autoregressive models in log-likelihood on natural image benchmarks.
UIUC, Adobe Research and University of Oregon propose HDMatt, a Deep Learning-based image matting Cross-Patch Context module for high-resolution image inputs.
“Unselfie,” a novel photographic transformation model that can automatically translate selfies into neutral-pose portraits.
ACM SIGGRAPH has honoured MIT CSAIL postdoctoral researcher Li Tzu-Mao with its 2020 Doctoral Dissertation Award for his PhD thesis Differentiable Visual Computing.
In a bid to generate high-resolution images showing realistic daytime changes while keeping accurate scene semantics, researchers have proposed a novel image-to-image translation model, HiDT (High Resolution Daytime Translation).
Researchers have introduced Active Neural SLAM, a modular and hierarchical approach to learning policies for exploring 3D environments.
Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.
Researchers from the South Korea-based tech company Hyperconnect recently proposed a new framework, MarioNETte, which aims to reenact target faces in a few-shot manner (from even a single image) while preserving identity without any fine-tuning.
A multi-institute research group recently released the paper PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer, which proposes a novel method for transferring makeup styles from a reference picture to a user’s source image.
The new AI-powered Animate feature on Meitu’s signature selfie app brings still pictures to life with floral auras, resplendent butterflies, flowing hair, petal bursts, flame nebulas, fireworks and more.
Although there are many means for identifying and locating a missing person — police and amber alerts, photos and biometrics such as fingerprints and even facial recognition — few of these resources are available for finding a lost dog, and about 75 percent of dogs in the USA are not microchipped.
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.
Alibaba’s R&D arm DAMO Academy announced yesterday that it is developing a new neural network chip called Ali-NPU for AI inferencing in the field of image processing, machine learning, etc.
Tencent Youtu — literally translated as “image optimization lab” — is the image processing, pattern recognition, machine learning, and data mining research arm of Chinese tech giant Tencent Group.
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