Search Results for author: Victor Lempitsky

Found 67 papers, 38 papers with code

DeepLandscape: Adversarial Modeling of Landscape Videos

1 code implementation ECCV 2020 Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, Victor Lempitsky

Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.

Video Generation

DINAR: Diffusion Inpainting of Neural Textures for One-Shot Human Avatars

no code implementations16 Mar 2023 David Svitov, Dmitrii Gudkov, Renat Bashirov, Victor Lempitsky

We present DINAR, an approach for creating realistic rigged fullbody avatars from single RGB images.

Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions

no code implementations7 Sep 2022 Egor Burkov, Ruslan Rakhimov, Aleksandr Safin, Evgeny Burnaev, Victor Lempitsky

Namely, we extend NeuS, a state-of-the-art neural implicit function formulation, to represent multiple objects of a class (human heads in our case) simultaneously.

3D Reconstruction

MegaPortraits: One-shot Megapixel Neural Head Avatars

no code implementations15 Jul 2022 Nikita Drobyshev, Jenya Chelishev, Taras Khakhulin, Aleksei Ivakhnenko, Victor Lempitsky, Egor Zakharov

In this work, we advance the neural head avatar technology to the megapixel resolution while focusing on the particularly challenging task of cross-driving synthesis, i. e., when the appearance of the driving image is substantially different from the animated source image.

Realistic One-shot Mesh-based Head Avatars

no code implementations16 Jun 2022 Taras Khakhulin, Vanessa Sklyarova, Victor Lempitsky, Egor Zakharov

We present a system for realistic one-shot mesh-based human head avatars creation, ROME for short.

NPBG++: Accelerating Neural Point-Based Graphics

1 code implementation CVPR 2022 Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev

We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time.

Novel View Synthesis

Perceptual Gradient Networks

1 code implementation5 May 2021 Dmitry Nikulin, Roman Suvorov, Aleksei Ivakhnenko, Victor Lempitsky

The use of perceptual loss however incurs repeated forward-backward passes in a large image classification network as well as a considerable memory overhead required to store the activations of this network.

Image Classification Image Generation

StylePeople: A Generative Model of Fullbody Human Avatars

1 code implementation CVPR 2021 Artur Grigorev, Karim Iskakov, Anastasia Ianina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, Victor Lempitsky

We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches.

Real-time RGBD-based Extended Body Pose Estimation

1 code implementation5 Mar 2021 Renat Bashirov, Anastasia Ianina, Karim Iskakov, Yevgeniy Kononenko, Valeriya Strizhkova, Victor Lempitsky, Alexander Vakhitov

We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera.

Pose Estimation

CNN with large memory layers

no code implementations27 Jan 2021 Rasul Karimov, Yury Malkov, Karim Iskakov, Victor Lempitsky

We have tested the memory layer on the classification, image reconstruction and relocalization problems and found that for some of those, the memory layers can provide significant speed/accuracy improvement with the high utilization of the key-value elements, while others require more careful fine-tuning and suffer from dying keys.

General Classification Image Classification +1

Relightable 3D Head Portraits from a Smartphone Video

no code implementations17 Dec 2020 Artem Sevastopolsky, Savva Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, Victor Lempitsky

The model is fitted to the sequence of frames with human face-specific priors that enforce the plausibility of albedo-lighting decomposition and operates at the interactive frame rate.


Image Generators with Conditionally-Independent Pixel Synthesis

2 code implementations CVPR 2021 Ivan Anokhin, Kirill Demochkin, Taras Khakhulin, Gleb Sterkin, Victor Lempitsky, Denis Korzhenkov

Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner.

Image Generation

TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderer

1 code implementation6 Sep 2020 Maria Kolos, Artem Sevastopolsky, Victor Lempitsky

New scenes can be modeled using gradient-based optimization of neural descriptors and of the rendering network.

Neural Rendering

DeepLandscape: Adversarial Modeling of Landscape Video

1 code implementation21 Aug 2020 Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, Victor Lempitsky

Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.

Neural Head Reenactment with Latent Pose Descriptors

2 code implementations CVPR 2020 Egor Burkov, Igor Pasechnik, Artur Grigorev, Victor Lempitsky

We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image.

Image Reconstruction Retrieval

Neural Point-Based Graphics

7 code implementations ECCV 2020 Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, Victor Lempitsky

A deep rendering network is learned in parallel with the descriptors, so that new views of the scene can be obtained by passing the rasterizations of a point cloud from new viewpoints through this network.

Learnable Triangulation of Human Pose

1 code implementation ICCV 2019 Karim Iskakov, Egor Burkov, Victor Lempitsky, Yury Malkov

We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views.

3D Human Pose Estimation

Instance Segmentation of Biological Images Using Harmonic Embeddings

1 code implementation CVPR 2020 Victor Kulikov, Victor Lempitsky

We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts.

Instance Segmentation Semantic Segmentation

Hyperbolic Image Embeddings

2 code implementations CVPR 2020 Valentin Khrulkov, Leyla Mirvakhabova, Evgeniya Ustinova, Ivan Oseledets, Victor Lempitsky

Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity).

Few-Shot Learning General Classification +3

Deep Neural Networks with Box Convolutions

1 code implementation NeurIPS 2018 Egor Burkov, Victor Lempitsky

Box filters computed using integral images have been part of the computer vision toolset for a long time.

Semantic Segmentation

Coordinate-based Texture Inpainting for Pose-Guided Image Generation

1 code implementation28 Nov 2018 Artur Grigorev, Artem Sevastopolsky, Alexander Vakhitov, Victor Lempitsky

Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.

Pose-Guided Image Generation Resynthesis

Image Manipulation with Perceptual Discriminators

no code implementations ECCV 2018 Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov, Victor Lempitsky

The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network.

Image Manipulation Translation

Instance Segmentation by Deep Coloring

1 code implementation26 Jul 2018 Victor Kulikov, Victor Yurchenko, Victor Lempitsky

We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation.

Autonomous Driving Instance Segmentation +1

Latent Convolutional Models

1 code implementation ICLR 2019 ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky

The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space.

Colorization Image Restoration

Impostor Networks for Fast Fine-Grained Recognition

no code implementations13 Jun 2018 Vadim Lebedev, Artem Babenko, Victor Lempitsky

In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms.

Deep Image Prior

11 code implementations CVPR 2018 Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

Image Denoising Image Inpainting +4

AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding

no code implementations ICCV 2017 Artem Babenko, Victor Lempitsky

To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords.


Product Split Trees

no code implementations CVPR 2017 Artem Babenko, Victor Lempitsky

In this work, we introduce a new kind of spatial partition trees for efficient nearest-neighbor search.


It Takes (Only) Two: Adversarial Generator-Encoder Networks

1 code implementation7 Apr 2017 Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning.

Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

2 code implementations ICCV 2017 Roman Klokov, Victor Lempitsky

We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.

3D Part Segmentation 3D Point Cloud Classification +2

Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

1 code implementation CVPR 2017 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 generation and image stylization problems.

Image Generation Image Stylization +1

Parsing Images of Overlapping Organisms with Deep Singling-Out Networks

no code implementations CVPR 2017 Victor Yurchenko, Victor Lempitsky

This work is motivated by the mostly unsolved task of parsing biological images with multiple overlapping articulated model organisms (such as worms or larvae).

Learnable Visual Markers

no code implementations NeurIPS 2016 Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky

We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative networks.

Pairwise Quantization

no code implementations5 Jun 2016 Artem Babenko, Relja Arandjelović, Victor Lempitsky

The proposed approach proceeds by finding a linear transformation of the data that effectively reduces the minimization of the pairwise distortions to the minimization of individual reconstruction errors.


Efficient Indexing of Billion-Scale Datasets of Deep Descriptors

no code implementations CVPR 2016 Artem Babenko, Victor Lempitsky

In this paper, we introduce a new dataset of one billion descriptors based on DNNs and reveal the relative inefficiency of IMI-based indexing for such descriptors compared to SIFT data.


Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

11 code implementations10 Mar 2016 Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example.

Style Transfer

Aggregating Local Deep Features for Image Retrieval

no code implementations ICCV 2015 Artem Babenko, Victor Lempitsky

Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems.

Image Classification Image Retrieval +1

Aggregating Deep Convolutional Features for Image Retrieval

2 code implementations26 Oct 2015 Artem Babenko, Victor Lempitsky

In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval.

Image Classification Image Retrieval +1

Fast ConvNets Using Group-wise Brain Damage

no code implementations CVPR 2016 Vadim Lebedev, Victor Lempitsky

We revisit the idea of brain damage, i. e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers.

Tree Quantization for Large-Scale Similarity Search and Classification

no code implementations CVPR 2015 Artem Babenko, Victor Lempitsky

We propose a new vector encoding scheme (tree quantization) that obtains lossy compact codes for high-dimensional vectors via tree-based dynamic programming.

Classification General Classification +3

Domain-Adversarial Training of Neural Networks

33 code implementations28 May 2015 Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky

Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.

Domain Generalization General Classification +5

Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

9 code implementations19 Dec 2014 Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, Victor Lempitsky

We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning.

General Classification Tensor Decomposition

Unsupervised Domain Adaptation by Backpropagation

21 code implementations26 Sep 2014 Yaroslav Ganin, Victor Lempitsky

Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).

Image Classification Multi-target Domain Adaptation +3

$ N^4 $-Fields: Neural Network Nearest Neighbor Fields for Image Transforms

no code implementations25 Jun 2014 Yaroslav Ganin, Victor Lempitsky

We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation.

Edge Detection Semantic Segmentation

Additive Quantization for Extreme Vector Compression

no code implementations CVPR 2014 Artem Babenko, Victor Lempitsky

We introduce a new compression scheme for high-dimensional vectors that approximates the vectors using sums of M codewords coming from M different codebooks.

General Classification Image Classification +1

Neural Codes for Image Retrieval

1 code implementation7 Apr 2014 Artem Babenko, Anton Slesarev, Alexandr Chigorin, Victor Lempitsky

In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e. g.\ Image-Net).

Dimensionality Reduction Image Retrieval +1

Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions

no code implementations7 Apr 2014 Artem Babenko, Victor Lempitsky

Here we introduce and evaluate two approximate nearest neighbor search systems that both exploit the synergy of product quantization processes in a more efficient way.

Data Compression Quantization

Learning to Detect Partially Overlapping Instances

no code implementations CVPR 2013 Carlos Arteta, Victor Lempitsky, J. A. Noble, Andrew Zisserman

For example, our detector can pick a region containing two or three object instances, while assigning such region an appropriate label.

object-detection Object Detection

Pylon Model for Semantic Segmentation

no code implementations NeurIPS 2011 Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman

Often, the random field is applied over a flat partitioning of the image into non-intersecting elements, such as pixels or super-pixels.

Image Segmentation Semantic Segmentation

Learning To Count Objects in Images

no code implementations NeurIPS 2010 Victor Lempitsky, Andrew Zisserman

Learning to infer such density can be formulated as a minimization of a regularized risk quadratic cost function.

Object Counting

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