Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Svitov, David"'
We present HAHA - a novel approach for animatable human avatar generation from monocular input videos. The proposed method relies on learning the trade-off between the use of Gaussian splatting and a textured mesh for efficient and high fidelity rend
Externí odkaz:
http://arxiv.org/abs/2404.01053
Autor:
Bashirov, Renat, Larionov, Alexey, Ustinova, Evgeniya, Sidorenko, Mikhail, Svitov, David, Zakharkin, Ilya, Lempitsky, Victor
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 realism. We use SMPL-X as a proxy geometry and render it with DNR (neural t
Externí odkaz:
http://arxiv.org/abs/2303.10275
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 SMPL-X body model to achieve photo-realistic quality of avatars while
Externí odkaz:
http://arxiv.org/abs/2303.09375
Autor:
Svitov, David, Alyamkin, Sergey
Convolutional neural networks (CNN) allow achieving the highest accuracy for the task of object detection in images. Major challenges in further development of object detectors are false-positive detections and high demand of processing power. In thi
Externí odkaz:
http://arxiv.org/abs/2011.07513
Autor:
Svitov, David, Alyamkin, Sergey
The usage of convolutional neural networks (CNNs) in conjunction with a margin-based softmax approach demonstrates a state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-ba
Externí odkaz:
http://arxiv.org/abs/2003.02586
Autor:
Alyamkin, Sergei, Ardi, Matthew, Berg, Alexander C., Brighton, Achille, Chen, Bo, Chen, Yiran, Cheng, Hsin-Pai, Fan, Zichen, Feng, Chen, Fu, Bo, Gauen, Kent, Goel, Abhinav, Goncharenko, Alexander, Guo, Xuyang, Ha, Soonhoi, Howard, Andrew, Hu, Xiao, Huang, Yuanjun, Kang, Donghyun, Kim, Jaeyoun, Ko, Jong Gook, Kondratyev, Alexander, Lee, Junhyeok, Lee, Seungjae, Lee, Suwoong, Li, Zichao, Liang, Zhiyu, Liu, Juzheng, Liu, Xin, Lu, Yang, Lu, Yung-Hsiang, Malik, Deeptanshu, Nguyen, Hong Hanh, Park, Eunbyung, Repin, Denis, Shen, Liang, Sheng, Tao, Sun, Fei, Svitov, David, Thiruvathukal, George K., Zhang, Baiwu, Zhang, Jingchi, Zhang, Xiaopeng, Zhuo, Shaojie
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisi
Externí odkaz:
http://arxiv.org/abs/1904.07714
Autor:
Alyamkin, Sergei, Ardi, Matthew, Brighton, Achille, Berg, Alexander C., Chen, Yiran, Cheng, Hsin-Pai, Chen, Bo, Fan, Zichen, Feng, Chen, Fu, Bo, Gauen, Kent, Go, Jongkook, Goncharenko, Alexander, Guo, Xuyang, Nguyen, Hong Hanh, Howard, Andrew, Huang, Yuanjun, Kang, Donghyun, Kim, Jaeyoun, Kondratyev, Alexander, Lee, Seungjae, Lee, Suwoong, Lee, Junhyeok, Liang, Zhiyu, Liu, Xin, Liu, Juzheng, Li, Zichao, Lu, Yang, Lu, Yung-Hsiang, Malik, Deeptanshu, Park, Eunbyung, Repin, Denis, Sheng, Tao, Shen, Liang, Sun, Fei, Svitov, David, Thiruvathukal, George K., Zhang, Baiwu, Zhang, Jingchi, Zhang, Xiaopeng, Zhuo, Shaojie
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short
Externí odkaz:
http://arxiv.org/abs/1810.01732
Autor:
Larionov, Alexey, Ustinova, Evgeniya, Sidorenko, Mikhail, Svitov, David, Zakharkin, Ilya, Lempitsky, Victor, Bashirov, Renat
We present a new approach for learning Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars can be rendered in real-time on mobile phones, have high realism, and can be learned from monocular videos. As in previous works, we use a combination of ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e386c3bfe6dc20b2096e91b2fcb88eeb
http://arxiv.org/abs/2303.10275
http://arxiv.org/abs/2303.10275
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