activity
20142023
most citedInstance Normalization: The Missing Ingredient for Fast Stylization

3.1k citations · 8.3k across the 43 of their papers we have counts for

collaborators

57 papers

cs.CV2023

MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video

Renat Bashirov, Alexey Larionov, Evgeniya Ustinova +4

We present a system to create Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars are rendered in real-time on mobile devices, learned from monocular videos, and have high reali…

cs.CV2023

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

David Svitov, Dmitrii Gudkov, Renat Bashirov +1

We present DINAR, an approach for creating realistic rigged fullbody avatars from single RGB images. Similarly to previous works, our method uses neural textures combined with the…

cs.CV2022★ 9 cited

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

Egor Burkov, Ruslan Rakhimov, Aleksandr Safin +2

We present an approach for the reconstruction of textured 3D meshes of human heads from one or few views. Since such few-shot reconstruction is underconstrained, it requires prior…

cs.CV2022

MegaPortraits: One-shot Megapixel Neural Head Avatars

Nikita Drobyshev, Jenya Chelishev, Taras Khakhulin +3

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 th…

cs.CV2022

Realistic One-shot Mesh-based Head Avatars

Taras Khakhulin, Vanessa Sklyarova, Victor Lempitsky +1

We present a system for realistic one-shot mesh-based human head avatars creation, ROME for short. Using a single photograph, our model estimates a person-specific head mesh and th…

cs.CV2022★ 4 cited

NPBG++: Accelerating Neural Point-Based Graphics

Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky +1

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