diff --git a/README.md b/README.md
index e0f73be..88b8cec 100644
--- a/README.md
+++ b/README.md
@@ -30,18 +30,23 @@ Our method converges very quickly. And achieves real-time rendering speed.
 ## Environmental Setups
 Please follow the [3D-GS](https://github.com/graphdeco-inria/gaussian-splatting) to install the relative packages.
 ```bash
-git clone https://github.com/hustvl/4DGaussians --recursive
+git clone https://github.com/hustvl/4DGaussians
 cd 4DGaussians
 conda create -n Gaussians4D python=3.7 
+conda activate Gaussians4D
+
 pip install -r requirements.txt
+cd submodules
+git clone https://github.com/ingra14m/depth-diff-gaussian-rasterization
+pip install -e depth-diff-gaussian-rasterization
 ```
-In our environment, we use pytorch=1.13.1+cu116
+In our environment, we use pytorch=1.13.1+cu116.
 ## Data Preparation
 **For synthetic scenes:**  
 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, twhen 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 offical websites, to save the memory, you should extract the frames of each video, them organize your dataset as follows.
 ```
 ├── data
 │   | dnerf 
diff --git a/requirements.txt b/requirements.txt
index 3c6bd34..2820ac9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,8 +2,7 @@ torch==1.13.1
 torchvision==0.14.1
 torchaudio==0.13.1
 mmcv==1.6.0
-matploblib
+matplotlib
 argparse
 lpips
 plyfile
-submodules/depth-diff-gaussian-rasterization
diff --git a/scene/deformation.py b/scene/deformation.py
index 89e1546..9419cfe 100644
--- a/scene/deformation.py
+++ b/scene/deformation.py
@@ -146,10 +146,6 @@ class deform_network(nn.Module):
         return self.deformation_net.get_mlp_parameters() + list(self.timenet.parameters())
     def get_grid_parameters(self):
         return self.deformation_net.get_grid_parameters()
-class Tineuvox(nn.Module):
-    def __init__(self) -> None:
-        super(Tineuvox).__init__()
-        pass
 
 def initialize_weights(m):
     if isinstance(m, nn.Linear):
diff --git a/scene/hexplane.py b/scene/hexplane.py
index 6835bb2..82d44f4 100644
--- a/scene/hexplane.py
+++ b/scene/hexplane.py
@@ -180,11 +180,3 @@ class HexPlaneField(nn.Module):
         features = self.get_density(pts, timestamps)
 
         return features
-if __name__ == "__main__":
-    aabb = torch.tensor([[-3,-3,-3],
-                         [3,3,3]])
-    planes = KPlaneField(aabb)
-    pts = torch.randn(10000,3)
-    time = torch.ones(10000,1)
-    features = planes.forward(pts,time)
-    print(features.shape)
diff --git a/scripts/train_dnerf_all.sh b/scripts/train_dnerf_all.sh
index cecd918..9d1cfa6 100644
--- a/scripts/train_dnerf_all.sh
+++ b/scripts/train_dnerf_all.sh
@@ -1,4 +1,4 @@
-bash scripts/train_ablation.sh dnerf_noboth
+bash scripts/process_dnerf.sh dnerf_tv_test
 wait
 # bash scripts/train_ablation.sh dnerf_3dgs
 # wait