Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Chen, Ronghan"'
This paper presents a Surface-Aligned Gaussian representation for creating animatable human avatars from monocular videos,aiming at improving the novel view and pose synthesis performance while ensuring fast training and real-time rendering. Recently
Externí odkaz:
http://arxiv.org/abs/2412.00845
Learning robust and generalizable manipulation skills from demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. While recent imitation learning methods have achieved impressive res
Externí odkaz:
http://arxiv.org/abs/2411.10203
Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly
Externí odkaz:
http://arxiv.org/abs/2404.00891
Autor:
Chen, Ronghan, Cong, Yang
Rotation-invariant (RI) 3D deep learning methods suffer performance degradation as they typically design RI representations as input that lose critical global information comparing to 3D coordinates. Most state-of-the-arts address it by incurring add
Externí odkaz:
http://arxiv.org/abs/2205.15210
Shape correspondence from 3D deformation learning has attracted appealing academy interests recently. Nevertheless, current deep learning based methods require the supervision of dense annotations to learn per-point translations, which severely overp
Externí odkaz:
http://arxiv.org/abs/2108.11609
Publikováno v:
npj Computational Materials. 2020, 6, 47
The proper design principles are essential for the efficient development of superionic conductors. However, the existing design principles are mainly proposed from the perspective of crystal structures. In this work, the face-centered cubic (fcc) ani
Externí odkaz:
http://arxiv.org/abs/1910.11545
Autor:
Tian, Ran, Chen, Ronghan, Xu, Zhenming, Wan, Songlin, Guan, Lin, Duan, Huanan, Li, Hua, Zhu, Hong, Sun, Di, Liu, Hezhou
Publikováno v:
In Carbon November 2019 152:503-510
Akademický článek
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Publikováno v:
IEEE Transactions on Cybernetics; 2023, Vol. 53 Issue: 3 p1682-1698, 17p
Akademický článek
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