From 5e9e171eb07df5fe4ef84a90434c397188b7ccde Mon Sep 17 00:00:00 2001
From: Phongsiri Nirachornkul
 <89494368+bring-nirachornkul@users.noreply.github.com>
Date: Thu, 27 Mar 2025 15:21:57 -0500
Subject: [PATCH] Update README.md

add meaning into extract and process data.
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 README.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 8643f0d..6584f73 100644
--- a/README.md
+++ b/README.md
@@ -70,15 +70,17 @@ Download SMPL+H mode from [SMPL+H](https://mano.is.tue.mpg.de/download.php) (cho
 
 ### Extract and Process Data
 
-You need to run the following scripts in order to obtain HumanML3D dataset:
+To generate the HumanML3D dataset, run these scripts in sequence:
 
-1. raw_pose_processing.ipynb
-2. motion_representation.ipynb
-3. cal_mean_variance.ipynb
+1. `raw_pose_processing.ipynb` - Standardizes raw poses from `./joints/` (scale, orientation, floor placement) and converts them into motion features for `./HumanML3D/new_joint_vecs/` and rebuilt joint positions for `./HumanML3D/new_joints/` using a skeleton model.
+2. `motion_representation.ipynb` - Adjusts raw joint data from `./joints/` (alignment, scaling) and creates motion features for `./HumanML3D/new_joint_vecs/` and 3D joint positions for `./HumanML3D/new_joints/` with kinematic transformations.
+3. `cal_mean_variance.ipynb` - Analyzes motion features from `./HumanML3D/new_joint_vecs/` (averages, spreads) and saves tuned mean and standard deviation as `Mean.npy` and `Std.npy` in `./HumanML3D/` for data preparation.
 
 This could be optional. Run it if you need animations. 
 
-4. animation.ipynb
+4. animation.ipynb - Visualizes 3D human motion data by creating an animated video from joint positions, plotting skeletal chains and a trajectory on a 3D grid. It saves the animation as a file (e.g., `.mp4`) using Matplotlib’s 3D plotting and animation tools.
+
+
 
 Please remember to go through the double-check steps. These aim to check if you are on the right track of obtaining HumanML3D dataset.