Zobrazeno 1 - 10
of 1 009
pro vyhledávání: '"Neverova IN"'
Autor:
Karaev, Nikita, Makarov, Iurii, Wang, Jianyuan, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of annotating real videos for this task. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. In orde
Externí odkaz:
http://arxiv.org/abs/2410.11831
Autor:
Bensadoun, Raphael, Monnier, Tom, Kleiman, Yanir, Kokkinos, Filippos, Siddiqui, Yawar, Kariya, Mahendra, Harosh, Omri, Shapovalov, Roman, Graham, Benjamin, Garreau, Emilien, Karnewar, Animesh, Cao, Ang, Azuri, Idan, Makarov, Iurii, Le, Eric-Tuan, Toisoul, Antoine, Novotny, David, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based
Externí odkaz:
http://arxiv.org/abs/2407.02599
Autor:
Siddiqui, Yawar, Monnier, Tom, Kokkinos, Filippos, Kariya, Mahendra, Kleiman, Yanir, Garreau, Emilien, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea, Shapovalov, Roman, Novotny, David
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object's appearance, AssetGen out
Externí odkaz:
http://arxiv.org/abs/2407.02445
Autor:
Bensadoun, Raphael, Kleiman, Yanir, Azuri, Idan, Harosh, Omri, Vedaldi, Andrea, Neverova, Natalia, Gafni, Oran
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for
Externí odkaz:
http://arxiv.org/abs/2407.02430
Autor:
Melas-Kyriazi, Luke, Laina, Iro, Rupprecht, Christian, Neverova, Natalia, Vedaldi, Andrea, Gafni, Oran, Kokkinos, Filippos
Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation is to fine-tune the
Externí odkaz:
http://arxiv.org/abs/2402.08682
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the
Externí odkaz:
http://arxiv.org/abs/2312.09222
Autor:
Shapovalov, Roman, Kleiman, Yanir, Rocco, Ignacio, Novotny, David, Vedaldi, Andrea, Chen, Changan, Kokkinos, Filippos, Graham, Ben, Neverova, Natalia
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially. Each scene is filmed in high production quality, from different viewpoints with several static cameras, as well as wearable action cameras, and record
Externí odkaz:
http://arxiv.org/abs/2307.12067
Autor:
Karaev, Nikita, Rocco, Ignacio, Graham, Benjamin, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
We introduce CoTracker, a transformer-based model that tracks a large number of 2D points in long video sequences. Differently from most existing approaches that track points independently, CoTracker tracks them jointly, accounting for their dependen
Externí odkaz:
http://arxiv.org/abs/2307.07635
Autor:
Karaev, Nikita, Rocco, Ignacio, Graham, Benjamin, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consisten
Externí odkaz:
http://arxiv.org/abs/2305.02296
Autor:
O. Yu. Vinogradova, M. M. Pankraskina, A. L. Neverova, D. I. Shikhbabaeva, M. A. Murzabekova, M. V. Chernikov, A. V. Popova, V. P. Kosenkova, L. B. Egoryan, V. V. Ptushkin
Publikováno v:
Онкогематология, Vol 19, Iss 3, Pp 16-33 (2024)
Background. Currently, targeted therapy is the most promising for the treatment of myelofibrosis (MF). Today, the results of many years of experience with the use of ruxolitinib, including outside randomized trials and the identification of predictor
Externí odkaz:
https://doaj.org/article/32b92dd0c58149d28216d7e1ea000c8a
