diff --git a/README.md b/README.md
index fc344fe..2adf4f4 100644
--- a/README.md
+++ b/README.md
@@ -56,7 +56,7 @@ 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 official websites. To save the memory, you should extract the frames of each video using `preprocess_dynerf.py` in the scripts and then 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
@@ -93,6 +93,18 @@ For training synthetic scenes such as `bouncingballs`, run
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 
 ```
 
+For training dynerf scenes such as `cut_roasted_beef`, run
+```python
+# First, extract the frames of each video.
+python scripts/preprocess_dynerf.py --datadir data/dynerf/cut_roasted_beef
+# Second, generate point clouds from input data.
+bash colmap.sh data/dynerf/cut_roasted_beef llff
+# Third, downsample the point clouds generated in the second step.
+python scripts/downsample_point.py data/dynerf/cut_roasted_beef/colmap/dense/workspace/fused.ply data/dynerf/cut_roasted_beef/points3D_downsample2.ply
+# Finally, train.
+python train.py -s data/dynerf/cut_roasted_beef --port 6017 --expname "dynerf/cut_roasted_beef" --configs arguments/dynerf/cut_roasted_beef.py 
+```
+
 You can customize your training config through the config files.
 
 Checkpoint
@@ -166,14 +178,6 @@ export exp_name="dynerf"
 python merge_many_4dgs.py --model_path output/$exp_name/sear_steak
 ```
 
-`preprocess_dynerf.py`:
-extract the frames of each video.
-usage:
-
-```
-python scripts/preprocess_dynerf.py --datadir data/dynerf/sear_steak
-```
-
 `colmap.sh`:
 generate point clouds from input data