18 citations · 26 across the 11 of their papers we have counts for
11 papers
MADrive: Memory-Augmented Driving Scene Modeling
Polina Karpikova, Daniil Selikhanovych, Kirill Struminsky +3
Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting rec…
One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
Daniil Selikhanovych, David Li, Aleksei Leonov +6
Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate d…
Inverse Bridge Matching Distillation
Nikita Gushchin, David Li, Daniil Selikhanovych +3
Learning diffusion bridge models is easy; making them fast and practical is an art. Diffusion bridge models (DBMs) are a promising extension of diffusion models for applications in…
A3D: Does Diffusion Dream about 3D Alignment?
Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…
Adversarial Schrödinger Bridge Matching
Nikita Gushchin, Daniil Selikhanovych, Sergei Kholkin +2
The Schrödinger Bridge (SB) problem offers a powerful framework for combining optimal transport and diffusion models. A promising recent approach to solve the SB problem is the Ite…
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
Savva Ignatyev, Daniil Selikhanovych, Oleg Voynov +4
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured. Our approach builds on two recent developments…
Extremal Domain Translation with Neural Optimal Transport
Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych +1
In many unpaired image domain translation problems, e.g., style transfer or super-resolution, it is important to keep the translated image similar to its respective input image. We…
Kernel Neural Optimal Transport
Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak qua…
Neural Optimal Transport
Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prov…
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution
Iaroslav Koshelev, Daniil Selikhanovych, Stamatios Lefkimmiatis
In this work, we study the problem of non-blind image deconvolution and propose a novel recurrent network architecture that leads to very competitive restoration results of high im…
The global rate of convergence for optimal tensor methods in smooth convex optimization
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov +3
We consider convex optimization problems with the objective function having Lipshitz-continuous -th order derivative, where . We propose a new tensor method, which clos…