Update README.md

README.md

@@ -9,12 +9,12 @@ This numpy version is faster(since some computation is re-wrote in a vectorized
 
 For more details about SMPL model, see [SMPL](http://smpl.is.tue.mpg.de/).
 
-Also, I provide a file `CMU_Mocap_Markers.pp`, which gives the correspondence between SMPL model and [CMU Mocap Dataset](http://mocap.cs.cmu.edu/) markers in .c3d files. For more detailes see the Usage section.
+Also, I provide a file `CMU_Mocap_Markers.pp`, which gives the correspondence between SMPL model and [CMU Mocap Dataset](http://mocap.cs.cmu.edu/) markers in .c3d files. For more details see the Usage section.
 
 ## Usage
 
 1. Download the model file [here](http://smpl.is.tue.mpg.de/downloads).
-2. Run `python preprocess.py /PATH/TO/THE/DOWNLOADED/MODEL` to preprocess the official model. `preprocess.py` will create a new file `model.pkl`. `smpl_np.py` and `smpl_tf.py` both rely on `model.pkl`. NOTE: the official pickle model contains `chumpy` object, so `prerocess.py` requires `chumpy` to extract official model.
+2. Run `python preprocess.py /PATH/TO/THE/DOWNLOADED/MODEL` to preprocess the official model. `preprocess.py` will create a new file `model.pkl`. `smpl_np.py` and `smpl_tf.py` both rely on `model.pkl`. **NOTE: the official pickle model contains `chumpy` object, so `prerocess.py` requires `chumpy` to extract official model.** Actually you need to modify chumpy's cource code a little bit to make it compatible to `preprocess.py`.
 3. Run `python smpl_np.py` to see the example.
 4. About `CMU_Mocap_Markers.pp`: you can first generate a standard SMPL model mesh(zero pose and zero beta), open it in MeshLab, and load this file in MeshLab. It gives 42 markers' position on the model surface. I simply mark these things by hand so there might be some small errors.