diff --git a/.gitignore b/.gitignore
index 9f73b0b..429c8c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -128,6 +128,7 @@ dmypy.json
# Pyre type checker
.pyre/
+docs/ppt/
checkpoints/
*.tar
*.zip
diff --git a/README.md b/README.md
index cc393b0..2c54df0 100644
--- a/README.md
+++ b/README.md
@@ -6,14 +6,14 @@
Currently, only the test code is available. Training scripts are coming soon
-[](https://github.com/neuralchen/SimSwap)
+[](https://github.com/neuralchen/SimSwap)
Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pdf) [[ACM DOI]](https://dl.acm.org/doi/10.1145/3394171.3413630)
-## Top News
+## Top News
**`2021-06-20`**: We release the scripts for arbitrary video and image processing, and a colab demo.
@@ -29,9 +29,9 @@ Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pd
- insightface
## Usage
-[Preparation](./doc/guidance/preparation.md)
+[Preparation](./docs/guidance/preparation.md)
-[Inference for image or video face swapping](./doc/guidance/usage.md)
+[Inference for image or video face swapping](./docs/guidance/usage.md)
[Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb)
@@ -39,24 +39,24 @@ Training: **coming soon**
## Video
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+ + 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. +
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+ 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.
+