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20162023
most citedLearning Deep Embeddings with Histogram Loss

261 citations · 373 across the 20 of their papers we have counts for

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Showing 2019 · cs.CVShow all

7 papers · 2 filters

cs.CV2019

Stereo relative pose from line and point feature triplets

Alexander Vakhitov, Victor Lempitsky, Yinqiang Zheng

Stereo relative pose problem lies at the core of stereo visual odometry systems that are used in many applications. In this work, we present two minimal solvers for the stereo rela…

cs.CV2019

Neural Point-Based Graphics

Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos +2

We present a new point-based approach for modeling the appearance of real scenes. The approach uses a raw point cloud as the geometric representation of a scene, and augments each…

cs.CV2019★ 16 cited

Textured Neural Avatars

Aliaksandra Shysheya, Egor Zakharov, Kara-Ali Aliev +9

We present a system for learning full-body neural avatars, i.e. deep networks that produce full-body renderings of a person for varying body pose and camera position. Our system ta…

cs.CV2019

Learnable Triangulation of Human Pose

Karim Iskakov, Egor Burkov, Victor Lempitsky +1

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. The first (b…

cs.CV2019

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

Egor Zakharov, Aliaksandra Shysheya, Egor Burkov +1

Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized…

cs.CV2019

Instance Segmentation of Biological Images Using Harmonic Embeddings

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. Unlike instance segment…