Modification due to BFM's License

BFM_Remove_Neck/readme.md

@@ -1,4 +1,4 @@
-The removed neck version of BFM can be downloaded in [**BaiduYun**](https://pan.baidu.com/s/1VhWYLpnxNBrlBg5_OKTojA) or [**GoogleDrive**](https://drive.google.com/open?id=1yi03Uq3j_GruOQGAfDi9_VzfYnALojrp).
+**Todo:** Update the neck-removing processing pipeline from original BFM model.
 
 The original version with neck:
 <p align="center">

readme.md

@@ -11,7 +11,6 @@
  - `2018.11.17`: Refine code and map the 3d vertex to original image space.
  - `2018.11.11`: **Update end-to-end inference pipeline: infer/serialize 3D face shape and 68 landmarks given one arbitrary image, please see readme.md below for more details.**
  - `2018.11.9`: Update trained model with higher performance in [models](./models).
- - `2018.11.9`: Add removed-neck version of BFM model in [BFM_Remove_Neck](./BFM_Remove_Neck).
  - `2018.10.4`: Add Matlab face mesh rendering demo in [visualize](./visualize).
  - `2018.9.9`: Add pre-process of face cropping in [benchmark](./benchmark).
 
@@ -153,7 +152,6 @@ The training scripts lie in `training` directory. The related resources are in b
 | train.configs | [BaiduYun](https://pan.baidu.com/s/1ozZVs26-xE49sF7nystrKQ) or [Google Drive](https://drive.google.com/open?id=1dzwQNZNMppFVShLYoLEfU3EOj3tCeXOD), 217M | The directory contraining 3DMM params and filelists of training dataset |
 | train_aug_120x120.zip | [BaiduYun](https://pan.baidu.com/s/19QNGst2E1pRKL7Dtx_L1MA) or [Google Drive](https://drive.google.com/open?id=17LfvBZFAeXt0ACPnVckfdrLTMHUpIQqE), 2.15G | The cropped images of augmentation training dataset |
 | test.data.zip | [BaiduYun](https://pan.baidu.com/s/1DTVGCG5k0jjjhOc8GcSLOw) or [Google Drive](https://drive.google.com/file/d/1r_ciJ1M0BSRTwndIBt42GlPFRv6CvvEP/view?usp=sharing), 151M | The cropped images of AFLW and ALFW-2000-3D testset |
-| model_refine.mat | [BaiduYun](https://pan.baidu.com/s/1VhWYLpnxNBrlBg5_OKTojA) or [Google Drive](https://drive.google.com/open?id=1yi03Uq3j_GruOQGAfDi9_VzfYnALojrp), 160M | BFM Model without neck part|
 
 After preparing the training dataset and configuration files, go into `training` directory and run the bash scripts to train. 
 The training parameters are all presented in bash scripts.