Artem Sevastopolsky

I am a Ph.D. candidate at Skoltech and a deep learning engineer at the Vision, Learning and Telepresence lab at Samsung AI Center Moscow, where I work on computer vision tasks related to human telepresence under the supervision of Prof. Victor Lempitsky.

At Samsung & Skoltech I worked on realistic differentiable rendering of 3D point clouds, human photo-to-texture synthesis, differentiable warping methods for view resynthesis and other projects. Previously, I was more into medical imaging at Lomonosov Moscow State University and in the industry.

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Featured Research

At the moment I'm mostly interested in applications of computer vision to 3D computer graphics and rendering.

Relightable 3D Head Portraits from a Smartphone Video
Artem Sevastopolsky, Savva Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, Victor Lempitsky
arXiv, 2020  
project page / video / arXiv / talk video

By taking a simple selfie-like capture by a smartphone, one can easily create a relightable 3D head portrait. The system is based on Neural Point-Based Graphics.

media coverage: technology.org (Jan '21)
TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderer
Maria Kolos*, Artem Sevastopolsky*, Victor Lempitsky
3DV, 2020  
project page / video / arXiv / code

An extension of Neural Point-Based Graphics that can render transparent objects, both synthetic and captured in-the-wild.

Neural point-based graphics
Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, Victor Lempitsky
ECCV, 2020
project page / video / arXiv / code

Given RGB(D) images and a point cloud reconstruction of a scene, our neural network generates extreme novel views of the scene which look highly photoreal.

Coordinate-based texture inpainting for pose-guided human image generation
Artur Grigorev, Artem Sevastopolsky, Alexander Vakhitov, Victor Lempitsky
CVPR, 2019
project page / arXiv / supmat

How would I look in a different pose? Or in different clothes? A ConvNet with coordinate-based texture inpainting to the rescue.

Stack-U-Net: refinement network for improved optic disc and cup image segmentation
Artem Sevastopolsky, Stepan Drapak, Konstantin Kiselev, Blake M. Snyder, Jeremy D. Keenan, Anastasia Georgievskaya
SPIE Medical Imaging, 2019  
arXiv

An advanced version of "Optic disc and cup segmentation methods..." (see below), where a segmentation is performed by a U-Net stacked multiple times, and a validation is performed on large amounts of data provided by UCSF.

Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network
Artem Sevastopolsky,
Pattern Recognition & Image Analysis, 2017  
arXiv / code

Automatic segmentation of two organs on an eye fundus image allows medical doctors to make more accurate early diagnosis of glaucoma and evaluate its progression over time.



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