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hassony2 / obman_render

[cvpr19] Code to generate images from the ObMan dataset, synthetic renderings of hands holding objects (or hands in isolation)
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cvpr-2019 cvpr19 grasping graspit hands rendering synthetic-data

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Learning Joint Reconstruction of Hands and Manipulated Objects - Demo, Training Code and Models

Yana Hasson, Gül Varol, Dimitris Tzionas, Igor Kalevatykh, Michael J. Black, Ivan Laptev, Cordelia Schmid, CVPR 2019

This code allows to generate synthetic images of hands holding objects as in the ObMan dataset.

In addition, hands-only images can also be generated, with hand-poses sampled randomly from the MANO hand pose space.

Examples of rendered images:

Hands+Objects Hands
handobject hand

Rendering generates:

For additional information about the project, see:

Installation

Setup blender

Clone repository

git clone https://github.com/hassony2/obman_render
cd obman_render

Download data dependencies

Download hand and object pickle data-structures

Download SURREAL assets

cd download
sh download_smpl_data.sh ../assets username password
cd ..

Download MANO model

Modify mano code to be Python3 compatible

-    print 'FINITO'
-    import cPickle as pickle
+    import pickle
-    smpl_data = pickle.load(open(fname_or_dict))
+    smpl_data = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')
-    dd = pickle.load(open(fname_or_dict))
+    dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')
-    with open('/is/ps2/dtzionas/mano/models/MANO_LEFT.pkl', 'rb') as f:
-        hand_l = load(f)
-    with open('/is/ps2/dtzionas/mano/models/MANO_RIGHT.pkl', 'rb') as f:
-        hand_r = load(f)
+    with open('/path/to/mano_v*_*/models/MANO_LEFT.pkl', 'rb') as f:
+        hand_l = load(f, encoding='latin1')
+    with open('/path/to/mano_v*_*/models/MANO_RIGHT.pkl', 'rb') as f:
+        hand_r = load(f, encoding='latin1')

At the time of writing the instructions mano version is 1.2 so use 

-    with open('/is/ps2/dtzionas/mano/models/MANO_LEFT.pkl', 'rb') as f:
-        hand_l = load(f)
-    with open('/is/ps2/dtzionas/mano/models/MANO_RIGHT.pkl', 'rb') as f:
-        hand_r = load(f)
+    with open('/path/to/mano_v1_2/models/MANO_LEFT.pkl', 'rb') as f:
+        hand_l = load(f, encoding='latin1')
+    with open('/path/to/mano_v1_2/models/MANO_RIGHT.pkl', 'rb') as f:
+        hand_r = load(f, encoding='latin1')

Download SMPL model

OPTIONAL : Download LSUN dataset (to generate images on LSUN backgrounds)

Download LSUN dataset following the instructions.

OPTIONAL : Download ImageNet dataset (to generate images on ImageNet backgrounds)

Download body+hand textures and grasp information

You should receive two links that will allow you to download bodywithands.zip and shapenet_grasps.zip.

cd assets/textures
mv path/to/downloaded/bodywithands.zip .
unzip bodywithands.zip
cd ../..
cd assets/grasps
mv path/to/downloaded/shapenet_grasps.zip .
unzip shapenet_grasps.zip
cd ../../
obman_render/
  assets/
    models/
      SMPLH_female.pkl
      basicModel_f_lbs_10_207_0_v1.0.2.fbx'
      basicModel_m_lbs_10_207_0_v1.0.2.fbx'
      ...
    grasps/
      shapenet_grasps/
      shapenet_grasps_splits.csv
    SURREAL/
      smpl_data/
        smpl_data.npz
    ...

Launch !

Minimal version on white background

Hands only

path/to/blender -noaudio -t 1 -P blender_grasps_sacred.py -- '{"frame_nb": 10, "frame_start": 0, "results_root": "datageneration/tmp", "background_datasets": ["white"]}'

Grasping objects

path/to/blender -noaudio -t 1 -P blender_hands_sacred.py -- '{"frame_nb": 10, "frame_start": 0, "results_root": "datageneration/tmp", "background_datasets": ["white"]}'

Full version with image backgrounds

Hands only

path/to/blender -noaudio -t 1 -P blender_hands_sacred.py -- '{"frame_nb": 10, "frame_start": 0, "results_root": "datageneration/tmp", "background_datasets": ["lsun", "imagenet"], "imagenet_path": "/path/to/imagenet", "lsun_path": "/path/to/lsun"}'

Grasping objects

path/to/blender -noaudio -t 1 -P blender_grasps_sacred.py -- '{"frame_nb": 10, "frame_start": 0, "results_root": "datageneration/tmp", "background_datasets": ["lsun", "imagenet"], "imagenet_path": "/path/to/imagenet", "lsun_path": "/path/to/lsun"}'

Citations

If you find this code useful for your research, consider citing:

@INPROCEEDINGS{hasson19_obman,
  title     = {Learning joint reconstruction of hands and manipulated objects},
  author    = {Hasson, Yana and Varol, G{\"u}l and Tzionas, Dimitris and Kalevatykh, Igor and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},
  booktitle = {CVPR},
  year      = {2019}
}
@INPROCEEDINGS{varol17_surreal,  
  title     = {Learning from Synthetic Humans},  
  author    = {Varol, G{\"u}l and Romero, Javier and Martin, Xavier and Mahmood, Naureen and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},  
  booktitle = {CVPR},  
  year      = {2017}  
}
@article{MANO:SIGGRAPHASIA:2017,
  title = {Embodied Hands: Modeling and Capturing Hands and Bodies Together},
  author = {Romero, Javier and Tzionas, Dimitrios and Black, Michael J.},
  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
  publisher = {ACM},
  month = nov,
  year = {2017},
  url = {http://doi.acm.org/10.1145/3130800.3130883},
  month_numeric = {11}
}