Learning Joint Reconstruction of Hands and Manipulated Objects - ObMan dataset

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

• Project page
• Training code and model
• GraspIt grasps generation using MANO
• Dataset generation

Download required files

Download dataset images and data

• Request the dataset on the ObMan webpage. Note that the data falls under the following license
• unzip obman.zip to /path/to/obman
• Your dataset structure should look like

obman/
  test/
    rgb/
    rgb_obj/
    meta/
    ...
  val/
    rgb/
    rgb_obj/
    meta/
    ...

Download object meshes

• Download object models from ShapeNet
  • Create an account on shapenet.org
  • Download models from download page

Download code

git clone https://github.com/hassony2/obman

cd obman

Load samples

python readataset --root /path/to/obman --shapenet_root /path/to/ShapeNetCore.v2 --split test --viz

Options you might be interested in --segment which keeps only the foreground --mini_factor 0.01 to load only 1% of the data (to speed-up loading)

Preprocess shapenet objects for training

Sample points on the external surface of the object:

python shapenet_samplepoints.py

Visualizations

Hand object and mesh in camera coordinates

Projected in pixel space

Hand vertices in blue, object vertices in red.

Citations

If you find this dataset 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}
}