Zobrazeno 1 - 10
of 3 179
pro vyhledávání: '"Wetzstein, A."'
Autor:
Nakayama, Kiyohiro, Ackermann, Jan, Kesdogan, Timur Levent, Zheng, Yang, Korosteleva, Maria, Sorkine-Hornung, Olga, Guibas, Leonidas J., Yang, Guandao, Wetzstein, Gordon
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming process, largely due to the manual work involved in designing them. To simpl
Externí odkaz:
http://arxiv.org/abs/2412.03937
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
Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving g
Externí odkaz:
http://arxiv.org/abs/2411.18616
We provide a proof of the BRST Noether 1.5th theorem, conjecture in [JHEP 10 (2024) 055], for a broad class of rank-1 BV theories including supergravity and 2-form gauge theories. The theorem asserts that the BRST Noether current of any BRST invarian
Externí odkaz:
http://arxiv.org/abs/2411.17829
Autor:
Kuang, Zhengfei, Zhang, Tianyuan, Zhang, Kai, Tan, Hao, Bi, Sai, Hu, Yiwei, Xu, Zexiang, Hasan, Milos, Wetzstein, Gordon, Luan, Fujun
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated
Externí odkaz:
http://arxiv.org/abs/2411.17249
Volume parameterizations abound in recent literature, from the classic voxel grid to the implicit neural representation and everything in between. While implicit representations have shown impressive capacity and better memory efficiency compared to
Externí odkaz:
http://arxiv.org/abs/2411.13525
Autor:
Chen, Hansheng, Shen, Bokui, Liu, Yulin, Shi, Ruoxi, Zhou, Linqi, Lin, Connor Z., Gu, Jiayuan, Su, Hao, Wetzstein, Gordon, Guibas, Leonidas
Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address th
Externí odkaz:
http://arxiv.org/abs/2410.18974
Autor:
Li, Zilu, Yang, Guandao, Zhao, Qingqing, Deng, Xi, Guibas, Leonidas, Hariharan, Bharath, Wetzstein, Gordon
Publikováno v:
SIGGRAPH Conference Papers 2024
This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integration, but they hinge on finding a function that both correlates with the
Externí odkaz:
http://arxiv.org/abs/2409.15394
Autor:
Je, Jihyeon, Liu, Jiayi, Yang, Guandao, Deng, Boyang, Cai, Shengqu, Wetzstein, Gordon, Litany, Or, Guibas, Leonidas
Symmetries are ubiquitous across all kinds of objects, whether in nature or in man-made creations. While these symmetries may seem intuitive to the human eye, detecting them with a machine is nontrivial due to the vast search space. Classical geometr
Externí odkaz:
http://arxiv.org/abs/2410.02786
Emerging holographic display technology offers unique capabilities for next-generation virtual reality systems. Current holographic near-eye displays, however, only support a small \'etendue, which results in a direct tradeoff between achievable fiel
Externí odkaz:
http://arxiv.org/abs/2409.03143