- RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling
-Xuanhong Chen*, Kairui Feng*, Naiyuan Liu**, Yifan Lu**, Zhengyan Tong, Bingbing Ni, Ziang Liu, Ning Lin
-Shanghai Jiao Tong University, Princeton University & University of Technology Sydney
-*Equal contribution.
-**Equal contribution.
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+ SimSwap: An Efficient Framework For High Fidelity Face Swapping
+Renwang Chen*, Xuanhong Chen*, Bingbing Ni, Yanhao Ge
+Shanghai Jiao Tong University, Tencent, China
+*Equal contribution.
+Abstract
+ +Abstract
+- Spatial Precipitation Downscaling is one of the most important problems in the geo-science community. However, it still remains an unaddressed issue. - Deep learning is a promising potential solution for downscaling. - In order to facilitate the research on precipitation downscaling for deep learning, - we present the first REAL (non-simulated) Large-Scale Spatial Precipitation Downscaling Dataset, - RainNet, which contains 62,424 pairs of low-resolution and high-resolution precipitation maps for 17 years. - Contrary to simulated data, this real dataset covers various types of real meteorological phenomena (e.g., Hurricane, Squall, etc.), - and shows the physical characters -- Temporal Misalignment, Temporal Sparse and Fluid Properties -- that challenge the downscaling algorithms. - In order to fully explore potential downscaling solutions, we propose an implicit physical estimation framework to learn the above characteristics. - Eight metrics specifically considering the physical property of the data set are raised, while fourteen models are evaluated on the proposed dataset. - Finally, we analyze the effectiveness and feasibility of these models on precipitation downscaling task. -
++ We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary + identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the + target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using + this module, we extend the architecture of an identityspecific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve + the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art + methods. The code is already available on github: https://github.com/neuralchen/SimSwap. +
+Samples in RainNet
+High Resolution Precipitation Maps -
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- Low Resolution Precipitation Maps -
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- High Resolution Precipitation Maps
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+ SimSwap Demo
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SimSwap Demo
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+ Single Face Video Swap +
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+ Multiple Faces Video Swap +
Low Resolution Precipitation Maps
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+ [TODO]
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- Downloads
+An overview of model capacity +
+ Citation
- title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling},
- author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing Ni and Ziang Liu and Ning Lin},
- year={2020},
- eprint={2012.09700},
- archivePrefix={arXiv},
- primaryClass={cs.CV}
- } -
+ Contact
+Please concat Kairui Feng kairuif@princeton.com, - Xuanhong Chen xuanhongchenzju@outlook.com, - Naiyuan Liu naiyuan.liu@student.uts.edu.au and - Yifan Lu yifan_lu@sjtu.edu.cn - for questions about the dataset.
+Downloads
+title={SimSwap}, +
ISBN={9781450379885}, +
url={http://dx.doi.org/10.1145/3394171.3413630}, +
DOI={10.1145/3394171.3413630}, +
journal={Proceedings of the 28th ACM International Conference on Multimedia}, +
publisher={ACM}, +
author={Chen, Renwang and Chen, Xuanhong and Ni, Bingbing and Ge, Yanhao}, +
year={2020}, +
month={Oct} +
} +
Contact
+Please concat Renwang Chen applebananac@sjtu.edu.cn and Xuanhong Chen xuanhongchenzju@outlook.com for questions about the paper.
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