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.
no code implementations • 17 Mar 2023 • Alexey Larionov, Evgeniya Ustinova, Mikhail Sidorenko, David Svitov, Ilya Zakharkin, Victor Lempitsky, Renat Bashirov
We present a new approach for learning Mobile Realistic Fullbody (MoRF) avatars.
no code implementations • 16 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.
no code implementations • 7 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.
no code implementations • 15 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.
no code implementations • 16 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.
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.
2 code implementations • CVPR 2022 • Taras Khakhulin, Denis Korzhenkov, Pavel Solovev, Gleb Sterkin, Timotei Ardelean, Victor Lempitsky
The second stage infers the color and the transparency values for these layers producing the final representation for novel view synthesis.
Ranked #1 on Novel View Synthesis on SWORD
4 code implementations • 15 Sep 2021 • Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Ranked #3 on Seeing Beyond the Visible on KITTI360-EX
1 code implementation • 5 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.
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.
1 code implementation • ICCV 2021 • Ilya Zakharkin, Kirill Mazur, Artur Grigorev, Victor Lempitsky
We propose a new approach to human clothing modeling based on point clouds.
1 code implementation • 5 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.
no code implementations • 27 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.
no code implementations • 17 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.
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.
Ranked #1 on Image Generation on Satellite-Landscapes 256 x 256
1 code implementation • 6 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.
1 code implementation • ECCV 2020 • Egor Zakharov, Aleksei Ivakhnenko, Aliaksandra Shysheya, Victor Lempitsky
The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views.
1 code implementation • 21 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.
no code implementations • ICCV 2021 • Kirill Mazur, Victor Lempitsky
We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks.
Ranked #25 on Semantic Segmentation on S3DIS
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.
1 code implementation • CVPR 2020 • Ivan Anokhin, Pavel Solovev, Denis Korzhenkov, Alexey Kharlamov, Taras Khakhulin, Alexey Silvestrov, Sergey Nikolenko, Victor Lempitsky, Gleb Sterkin
We present the high-resolution daytime translation (HiDT) model for this task.
3 code implementations • ECCV 2018 • Alexander Vakhitov, Victor Lempitsky, Yinqiang Zheng
In this work, we present two minimal solvers for the stereo relative pose.
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.
no code implementations • CVPR 2019 • Aliaksandra Shysheya, Egor Zakharov, Kara-Ali Aliev, Renat Bashirov, Egor Burkov, Karim Iskakov, Aleksei Ivakhnenko, Yury Malkov, Igor Pasechnik, Dmitry Ulyanov, Alexander Vakhitov, Victor Lempitsky
In particular, our system estimates an explicit two-dimensional texture map of the model surface.
6 code implementations • ICCV 2019 • Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor Lempitsky
In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.
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.
Ranked #2 on 3D Human Pose Estimation on Human3.6M
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.
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).
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.
1 code implementation • 28 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.
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.
1 code implementation • 26 Jul 2018 • Victor Kulikov, Victor Yurchenko, Victor Lempitsky
We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation.
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.
no code implementations • 13 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.
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.
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
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.
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.
1 code implementation • 7 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.
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.
Ranked #50 on 3D Part Segmentation on ShapeNet-Part
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.
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).
no code implementations • 17 Nov 2016 • Andrey Kuzmin, Dmitry Mikushin, Victor Lempitsky
We present a new deep learning-based approach for dense stereo matching.
1 code implementation • NeurIPS 2016 • Evgeniya Ustinova, Victor Lempitsky
We suggest a loss for learning deep embeddings.
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.
21 code implementations • 27 Jul 2016 • Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
It this paper we revisit the fast stylization method introduced in Ulyanov et.
no code implementations • 25 Jul 2016 • Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, Victor Lempitsky
In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze.
no code implementations • 5 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.
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.
11 code implementations • 10 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.
1 code implementation • 16 Dec 2015 • Evgeniya Ustinova, Yaroslav Ganin, Victor Lempitsky
In this work we propose a new architecture for person re-identification.
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.
2 code implementations • 26 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.
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.
no code implementations • CVPR 2015 • Daniil Kononenko, Victor Lempitsky
We revisit the well-known problem of gaze correction and present a solution based on supervised machine learning.
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.
33 code implementations • 28 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.
Ranked #2 on Domain Adaptation on Synth Digits-to-SVHN
9 code implementations • 19 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.
21 code implementations • 26 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).
Ranked #1 on Domain Adaptation on UCF-to-Olympic
no code implementations • 25 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.
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.
1 code implementation • 7 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).
no code implementations • 7 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.
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.
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.
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.