Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Park, Sungheon"'
Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feat
Externí odkaz:
http://arxiv.org/abs/2302.09311
The method of neural radiance fields (NeRF) has been developed in recent years, and this technology has promising applications for synthesizing novel views of complex scenes. However, NeRF requires dense input views, typically numbering in the hundre
Externí odkaz:
http://arxiv.org/abs/2211.12758
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths. Recently, there have been some approaches that incorporate the problem
Externí odkaz:
http://arxiv.org/abs/2007.10961
For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flo
Externí odkaz:
http://arxiv.org/abs/1905.09500
Autor:
Park, Sungheon, Kwak, Nojun
In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks. We adopted the structure of the relational networks in order to capture the relations among different body parts. In our method, each p
Externí odkaz:
http://arxiv.org/abs/1805.08961
In this paper, we propose a simple yet effective method for multiple music source separation using convolutional neural networks. Stacked hourglass network, which was originally designed for human pose estimation in natural images, is applied to a mu
Externí odkaz:
http://arxiv.org/abs/1805.08559
While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using
Externí odkaz:
http://arxiv.org/abs/1608.03075
Akademický článek
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Autor:
Park, Sungheon, Kwak, Nojun
Publikováno v:
2015 IEEE International Conference on Image Processing (ICIP); 2015, p1910-1914, 5p
