4 citations · 5 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models
Vladislav Pyatov, Gleb Bobrovskikh, Saveliy Galochkin +6
We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to s…
ATATA: One Algorithm to Align Them All
Boyi Pang, Savva Ignatyev, Vladimir Ippolitov +8
We suggest a new multi-modal algorithm for joint inference of paired structurally aligned samples with Rectified Flow models. While some existing methods propose a codependent gene…
G-CUT3R: Guided 3D Reconstruction with Camera and Depth Prior Integration
Ramil Khafizov, Artem Komarichev, Ruslan Rakhimov +2
We introduce G-CUT3R, a novel feed-forward approach for guided 3D scene reconstruction that enhances the CUT3R model by integrating prior information. Unlike existing feed-forward…
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…
T-3DGS: Removing Transient Objects for 3D Scene Reconstruction
Alexander Markin, Vadim Pryadilshchikov, Artem Komarichev +3
Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions. To address this challenge, we propose T-3DGS, a novel framework that robustl…