add Human3.6M support for deep_mimic env

examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/README.md

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+# Support for Human3.6M Dataset
+
+Developer: Somedaywilldo (somedaywilldo@foxmail.com)
+
+### Inverse Kinect and Reference Rendering
+
+If you want to learn movements directly from coordinates, you can use **inverse_kinect.py** and **reder_reference.py**, currently it just support a dataset created by [VideoPose3D](https://github.com/facebookresearch/VideoPose3D).
+
+Download the pre-prosessed Human3.6M dataset of Videopose3D at [here](https://www.dropbox.com/s/z5bwig0h6mww590/data_3d_h36m.npz?dl=0).
+
+After downloading the **data_3d_h36m.npz** file to this directory, then you can try to run this command: 
+
+```shell
+$ python render_reference.py \
+	--dataset_path=<path to data_3d_h36m.npz> \
+	--subject=S11 \
+    --action=Walking \
+	--json_path=<path to save Walking.json> \
+    --fps=24 \
+    --loop=wrap  \
+    --draw_gt
+```
+
+"--draw_gt" will draw the ground truth using **pybullet.addUserDebugLine()**, the right part of the humanoid lines will be red, other parts will be black. This is just for debugging, the render process will be much faster without the '--draw_gt' flag.
+
+
+
+If no errors shows, it should be look like this [video](https://www.youtube.com/watch?v=goew_FmUtOE).
+
+### Contact
+
+Inverse kinect and reference rendering module is developed by Somedaywilldo.
+
+Email: somedaywilldo@foxmail.com
+
+### Reference
+
+[1] DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills [[Link](https://arxiv.org/abs/1804.02717)]
+
+[2] 3D human pose estimation in video with temporal convolutions and semi-supervised training [[Link](https://arxiv.org/abs/1811.11742)]
+
+
+