diff --git a/README.md b/README.md
index 60c975f..ab87afa 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 # 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
 
-## Arxiv Preprint
+## arXiv Preprint
 
-### [Project Page](https://guanjunwu.github.io/4dgs/index.html)| [Arxiv Paper](https://arxiv.org/abs/2310.08528)
+### [Project Page](https://guanjunwu.github.io/4dgs/index.html)| [arXiv Paper](https://arxiv.org/abs/2310.08528)
 
 
 [Guanjun Wu](https://guanjunwu.github.io/)<sup>1*</sup>, [Taoran Yi](https://github.com/taoranyi)<sup>2*</sup>,
@@ -13,7 +13,7 @@
 ---------------------------------------------------
 
 ![block](assets/teaserfig.png)   
-Our method converges very quickly. And achieves real-time rendering speed.
+Our method converges very quickly and achieves real-time rendering speed.
 
 Colab demo:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/4DGaussians-colab/blob/main/4DGaussians_colab.ipynb) (Thanks [camenduru](https://github.com/camenduru/4DGaussians-colab).)
 
@@ -45,7 +45,7 @@ In our environment, we use pytorch=1.13.1+cu116.
 The dataset provided in [D-NeRF](https://github.com/albertpumarola/D-NeRF) is used. You can download the dataset from [dropbox](https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0).
 
 **For real dynamic scenes:**  
-The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used. You can download scenes from [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1) and organize them as [Nerfies](https://github.com/google/nerfies#datasets). Meanwhile, [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video) could be downloaded from their offical websites, to save the memory, you should extract the frames of each video, them organize your dataset as follows.
+The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used. You can download scenes from [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1) and organize them as [Nerfies](https://github.com/google/nerfies#datasets). Meanwhile, [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video) could be downloaded from their official websites. To save the memory, you should extract the frames of each video and then organize your dataset as follows.
 ```
 ├── data
 │   | dnerf 
@@ -79,7 +79,7 @@ For training synthetic scenes such as `lego`, run
 ``` 
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 
 ``` 
-You can custom your training config through the config files.
+You can customize your training config through the config files.
 ## Rendering
 Run the following script to render the images.  
 
@@ -89,7 +89,7 @@ python render.py --model_path "output/dnerf/bouncingballs/"  --skip_train --conf
 
 
 ## Evaluation
-Run the following script to evaluate the model.  
+You can just run the following script to evaluate the model.  
 
 ```
 python metrics.py --model_path "output/dnerf/bouncingballs/"