3.1k citations · 4.5k across the 9 of their papers we have counts for
11 papers
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors
Akhmedkhan Shabanov, Ilya Krotov, Nikolay Chinaev +7
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification. However, the quality of the captur…
Neural Point-Based Graphics
Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos +2
We present a new point-based approach for modeling the appearance of real scenes. The approach uses a raw point cloud as the geometric representation of a scene, and augments each…
Textured Neural Avatars
Aliaksandra Shysheya, Egor Zakharov, Kara-Ali Aliev +9
We present a system for learning full-body neural avatars, i.e. deep networks that produce full-body renderings of a person for varying body pose and camera position. Our system ta…
Generative Models for Fast Calorimeter Simulation.LHCb case
Viktoria Chekalina, Elena Orlova, Fedor Ratnikov +3
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These…
Image Manipulation with Perceptual Discriminators
Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov +1
Systems that perform image manipulation using deep convolutional networks have achieved remarkable realism. Perceptual losses and losses based on adversarial discriminators are the…
Deep Image Prior
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic i…
It Takes (Only) Two: Adversarial Generator-Encoder Networks
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and uncondition…
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture genera…
Instance Normalization: The Missing Ingredient for Fast Stylization
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant quali…
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi +1
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and…
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov +2
Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Catego…