2.6k citations · 3.2k across the 12 of their papers we have counts for
12 papers
Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction
Vanessa Sklyarova, Jenya Chelishev, Andreea Dogaru +3
Generating realistic human 3D reconstructions using image or video data is essential for various communication and entertainment applications. While existing methods achieved impre…
Stereo Magnification with Multi-Layer Images
Taras Khakhulin, Denis Korzhenkov, Pavel Solovev +3
Representing scenes with multiple semi-transparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and…
Parsing Images of Overlapping Organisms with Deep Singling-Out Networks
Victor Yurchenko, Victor Lempitsky
This work is motivated by the mostly unsolved task of parsing biological images with multiple overlapping articulated model organisms (such as worms or larvae). We present a genera…
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Andrey Kuzmin, Dmitry Mikushin, Victor Lempitsky
We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employ…
Learning Deep Embeddings with Histogram Loss
Evgeniya Ustinova, Victor Lempitsky
We suggest a loss for learning deep embeddings. The new loss does not introduce parameters that need to be tuned and results in very good embeddings across a range of datasets and…
Learnable Visual Markers
Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative net…