update readme

README.md

@@ -1,23 +1,26 @@
-SMPL layer for PyTorch
+pose2smpl
 =======
 
-[SMPL](http://smpl.is.tue.mpg.de) human body [\[1\]](#references) layer for [PyTorch](https://pytorch.org/) (tested with v0.4 and v1.x)
+### Fitting SMPL Parameters by 3D-pose Key-points
-is a differentiable PyTorch layer that deterministically maps from pose and shape parameters to human body joints and vertices.
+
-It can be integrated into any architecture as a differentiable layer to predict body meshes.
+The repository provides a tool to fit **SMPL parameters** from **3D-pose** datasets that contain key-points of human body.
-The code is adapted from the [manopth](https://github.com/hassony2/manopth) repository by [Yana Hasson](https://github.com/hassony2).
+
+The SMPL human body layer for Pytorch is from the [smplpytorch](https://github.com/gulvarol/smplpytorch) repository.
 
 <p align="center">
-<img src="assets/image.png" alt="smpl" width="300"/>
+<img src="assets/fit.gif" width="350"/>
+<img src="assets/gt.gif" width="350"/>
 </p>
 
-
 ## Setup
 
 ### 1. The `smplpytorch` package
 * **Run without installing:** You will need to install the dependencies listed in [environment.yml](environment.yml):
+  
   * `conda env update -f environment.yml` in an existing environment, or
   * `conda env create -f environment.yml`, for a new `smplpytorch` environment
 * **Install:** To import `SMPL_Layer` in another project with `from smplpytorch.pytorch.smpl_layer import SMPL_Layer` do one of the following.
+  
   * Option 1: This should automatically install the dependencies.
     ``` bash
     git clone https://github.com/gulvarol/smplpytorch.git
@@ -33,35 +36,40 @@ The code is adapted from the [manopth](https://github.com/hassony2/manopth) repo
   * Download the models from the [SMPL website](http://smpl.is.tue.mpg.de/) by choosing "SMPL for Python users". Note that you need to comply with the [SMPL model license](http://smpl.is.tue.mpg.de/license_model).
   * Extract and copy the `models` folder into the `smplpytorch/native/` folder (or set the `model_root` parameter accordingly).
 
-## Demo
+### 3. Download Dataset
 
-Forward pass the randomly created pose and shape parameters from the SMPL layer and display the human body mesh and joints:
+- Download the datasets you want to fit
 
-`python demo.py`
+    currently supported datasets:
 
-## Acknowledgements
+    - [HumanAct12](https://ericguo5513.github.io/action-to-motion/)
-The code **largely** builds on the [manopth](https://github.com/hassony2/manopth) repository from [Yana Hasson](https://github.com/hassony2), which implements the [MANO](http://mano.is.tue.mpg.de) hand model [\[2\]](#references) layer.
+    - [UTD-MHAD](https://personal.utdallas.edu/~kehtar/UTD-MHAD.html)
     
-The code is a PyTorch port of the original [SMPL](http://smpl.is.tue.mpg.de) model from [chumpy](https://github.com/mattloper/chumpy). It builds on the work of [Loper](https://github.com/mattloper) et al. [\[1\]](#references).
+- Set the **DATASET.PATH** in the corresponding configuration file to the location of dataset.
 
-The code [reuses](https://github.com/gulvarol/smpl/pytorch/rodrigues_layer.py) [part of the code](https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py) by [Zhang Xiong](https://github.com/MandyMo) to compute the rotation utilities.
+## Fitting
 
-If you find this code useful for your research, please cite the original [SMPL](http://smpl.is.tue.mpg.de) publication:
+### 1. Executing Code
+
+You can start the fitting procedure by the following code and the configuration file in *fit/configs* corresponding to the dataset_name will be loaded:
 
 ```
-@article{SMPL:2015,
+python fit/tools/main.py --dataset_name [DATASET NAME] --dataset_path [DATASET PATH]
-    author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
-    title = {{SMPL}: A Skinned Multi-Person Linear Model},
-    journal = {ACM Trans. Graphics (Proc. SIGGRAPH Asia)},
-    number = {6},
-    pages = {248:1--248:16},
-    volume = {34},
-    year = {2015}
-}
 ```
 
-## References
+### 2. Output
+
+- **Direction**: The output SMPL parameters will be stored in *fit/output*
+
+- **Format:** The output are *.pkl* files, and the data format is:
+
+    ```
+    {
+    	"label": [The label of action],
+    	"pose_params": pose parameters of SMPL (shape = [frame_num, 72]),
+    	"shape_params": pose parameters of SMPL (shape = [frame_num, 10]),
+    	"Jtr": key-point coordinates of SMPL model (shape = [frame_num, 24, 3])
+    }
+    ```
 
-\[1\] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black, "SMPL: A Skinned Multi-Person Linear Model," SIGGRAPH Asia, 2015.
     
-\[2\] Javier Romero, Dimitrios Tzionas, and Michael J. Black, "Embodied Hands: Modeling and Capturing Hands and Bodies Together," SIGGRAPH Asia, 2017.

fit/tools/main.py

@@ -12,8 +12,9 @@ sys.path.append(os.getcwd())
 from smplpytorch.pytorch.smpl_layer import SMPL_Layer
 from train import train
 from transform import transform
-from save import save_params
+from save import save_pic, save_params
 from load import load
+import numpy as np
 torch.backends.cudnn.benchmark=True
 
 def parse_args():
@@ -101,6 +102,6 @@ if __name__ == "__main__":
                 logger,writer,device,
                 args,cfg)
             
-            # save_pic(res,smpl_layer,file,logger,args.dataset_name)
+            # save_pic(res,smpl_layer,file,logger,args.dataset_name,target)
             save_params(res,file,logger, args.dataset_name)
         

fit/tools/save.py

@@ -15,11 +15,13 @@ def create_dir_not_exist(path):
         os.mkdir(path)
 
 
-def save_pic(res, smpl_layer, file, logger, dataset_name):
+def save_pic(res, smpl_layer, file, logger, dataset_name,target):
     _, _, verts, Jtr = res
     file_name = re.split('[/.]', file)[-2]
     fit_path = "fit/output/{}/picture/fit/{}".format(dataset_name,file_name)
+    gt_path = "fit/output/{}/picture/gt/{}".format(dataset_name,file_name)
     create_dir_not_exist(fit_path)
+    create_dir_not_exist(gt_path)
     logger.info('Saving pictures at {}'.format(fit_path))
     for i in tqdm(range(Jtr.shape[0])):
         display_model(
@@ -32,6 +34,16 @@ def save_pic(res, smpl_layer, file, logger, dataset_name):
             batch_idx=i,
             show=False,
             only_joint=False)
+        display_model(
+            {'verts': verts.cpu().detach(),
+             'joints': target.cpu().detach()},
+            model_faces=smpl_layer.th_faces,
+            with_joints=True,
+            kintree_table=smpl_layer.kintree_table,
+            savepath=os.path.join(gt_path+"/frame_{}".format(i)),
+            batch_idx=i,
+            show=False,
+            only_joint=True)
     logger.info('Pictures saved')
 
 

fit/tools/train.py

@@ -73,7 +73,6 @@ def train(smpl_layer, target,
     
     early_stop = Early_Stop()
     for epoch in tqdm(range(cfg.TRAIN.MAX_EPOCH)):
-    # for epoch in range(cfg.TRAIN.MAX_EPOCH):
         verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
         loss = F.smooth_l1_loss(Jtr.index_select(1, index["smpl_index"]) * 100,
                                 target.index_select(1, index["dataset_index"]) * 100)

make_gif.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import imageio, os
 images = []
-filenames = sorted(fn for fn in os.listdir('./fit/output/UTD_MHAD/picture/') )
+filenames = sorted(fn for fn in os.listdir('./fit/output/HumanAct12/picture/fit/P01G01R01F0001T0064A0101') )
 for filename in filenames:
-    images.append(imageio.imread('./fit/output/UTD_MHAD/picture/fit/a10_s1_t1_skeleton/'+filename))
+    images.append(imageio.imread('./fit/output/HumanAct12/picture/fit/P01G01R01F0001T0064A0101/'+filename))
-imageio.mimsave('./fit.gif', images, duration=0.3)
+imageio.mimsave('./assets/fit.gif', images, duration=0.3)