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of 103
pro vyhledávání: '"Davtyan, Aram"'
In this work we propose a novel method for unsupervised controllable video generation. Once trained on a dataset of unannotated videos, at inference our model is capable of both composing scenes of predefined object parts and animating them in a plau
Externí odkaz:
http://arxiv.org/abs/2403.14368
The growing interest in novel view synthesis, driven by Neural Radiance Field (NeRF) models, is hindered by scalability issues due to their reliance on precisely annotated multi-view images. Recent models address this by fine-tuning large text2image
Externí odkaz:
http://arxiv.org/abs/2312.04337
Autor:
Davtyan, Aram, Favaro, Paolo
We propose a novel unsupervised method to autoregressively generate videos from a single frame and a sparse motion input. Our trained model can generate unseen realistic object-to-object interactions. Although our model has never been given the expli
Externí odkaz:
http://arxiv.org/abs/2306.03988
We introduce a novel generative model for video prediction based on latent flow matching, an efficient alternative to diffusion-based models. In contrast to prior work, we keep the high costs of modeling the past during training and inference at bay
Externí odkaz:
http://arxiv.org/abs/2211.14575
Autor:
Davtyan, Aram, Favaro, Paolo
We present GLASS, a method for Global and Local Action-driven Sequence Synthesis. GLASS is a generative model that is trained on video sequences in an unsupervised manner and that can animate an input image at test time. The method learns to segment
Externí odkaz:
http://arxiv.org/abs/2204.06558
Autor:
Davtyan, Aram, Sameni, Sepehr, Cerkezi, Llukman, Meishvilli, Givi, Bielski, Adam, Favaro, Paolo
Optimization is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training neural networks the loss function changes over (iteration) time due to the randomized
Externí odkaz:
http://arxiv.org/abs/2107.03331
Akademický článek
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Theoretical Insights into Mechanisms of Stochastic Gating in Channel-Facilitated Molecular Transport
Autor:
Davtyan, Aram, Kolomeisky, Anatoly B.
Molecular motion through pores plays a crucial role in various natural and industrial processes. One of the most fascinating features of biological channel-facilitated transport is a stochastic gating process, when the channels dynamically fluctuate
Externí odkaz:
http://arxiv.org/abs/1812.07693
Autor:
Xu, Yuechuan, Knapp, Kaitlin, Le, Kyle N., Schafer, Nicholas P., Safari, Mohammad S., Davtyan, Aram, Wolynes, Peter G., Vekilov, Peter G.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Sep . 118(38), 1-7.
Externí odkaz:
https://www.jstor.org/stable/27075627
Autor:
Yang, David S., Saeedi, Arash, Davtyan, Aram, Fathi, Mohsen, Sherman, Michael B., Safari, Mohammad S., Klindziuk, Alena, Barton, Michelle C., Varadarajan, Navin, Kolomeisky, Anatoly B., Vekilov, Peter G.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Mar 01. 118(10), 1-10.
Externí odkaz:
https://www.jstor.org/stable/27027460