Zobrazeno 1 - 10
of 106 195
pro vyhledávání: '"Gaussians"'
Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficien
Externí odkaz:
http://arxiv.org/abs/2412.13983
Autor:
Xu, Xinli, Ge, Wenhang, Qiu, Dicong, Chen, ZhiFei, Yan, Dongyu, Liu, Zhuoyun, Zhao, Haoyu, Zhao, Hanfeng, Zhang, Shunsi, Liang, Junwei, Chen, Ying-Cong
Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this area remains under-explored due
Externí odkaz:
http://arxiv.org/abs/2412.11258
Autor:
Liang, Siyun, Wang, Sen, Li, Kunyi, Niemeyer, Michael, Gasperini, Stefano, Navab, Nassir, Tombari, Federico
3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While the vanilla Gaussian Splatting representation is mainly designed for view synthesis, more recent works investigated how to extend it with sce
Externí odkaz:
http://arxiv.org/abs/2412.10231
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details due to
Externí odkaz:
http://arxiv.org/abs/2412.06234
The quasi-2D electrostatic systems, characterized by periodicity in two dimensions with a free third dimension, have garnered significant interest in many fields. We apply the sum-of-Gaussians (SOG) approximation to the Laplace kernel, dividing the i
Externí odkaz:
http://arxiv.org/abs/2412.04595
This paper introduces a novel clothed human model that can be learned from multiview RGB videos, with a particular emphasis on recovering physically accurate body and cloth movements. Our method, Position Based Dynamic Gaussians (PBDyG), realizes ``m
Externí odkaz:
http://arxiv.org/abs/2412.04433
Autor:
Gao, Qiankun, Wu, Yanmin, Wen, Chengxiang, Meng, Jiarui, Tang, Luyang, Chen, Jie, Wang, Ronggang, Zhang, Jian
Reconstructing dynamic scenes with large-scale and complex motions remains a significant challenge. Recent techniques like Neural Radiance Fields and 3D Gaussian Splatting (3DGS) have shown promise but still struggle with scenes involving substantial
Externí odkaz:
http://arxiv.org/abs/2412.02493
Autor:
Deng, Junli, Luo, Yihao
Dynamic scene rendering has taken a leap forward with the rise of 4D Gaussian Splatting, but there's still one elusive challenge: how to make 3D Gaussians move through time as naturally as they would in the real world, all while keeping the motion sm
Externí odkaz:
http://arxiv.org/abs/2412.00333
Autor:
Chao, Brian, Tseng, Hung-Yu, Porzi, Lorenzo, Gao, Chen, Li, Tuotuo, Li, Qinbo, Saraf, Ayush, Huang, Jia-Bin, Kopf, Johannes, Wetzstein, Gordon, Kim, Changil
3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in
Externí odkaz:
http://arxiv.org/abs/2411.18625
State-of-the-art novel view synthesis methods such as 3D Gaussian Splatting (3DGS) achieve remarkable visual quality. While 3DGS and its variants can be rendered efficiently using rasterization, many tasks require access to the underlying 3D surface,
Externí odkaz:
http://arxiv.org/abs/2411.16898