CAMS

This is a PyTorch implementation of CAMS.

Environment

Install PyTorch and most other packages we use are listed in environment.yml. We use the implementation of MANO hand from manotorch.

Data Preparation

MANO assets

This is used for synthesizing and evaluation. Please follow these steps:

1. Download the mano_assets.zip
2. Place it under the folder data/ and unzip it, then it should be like data/mano_assets

HOI4D CAMS version

This is the dataset we use.

How to use

1. Download the meta file, eg. pliers_meta.torch
2. Place it under the folder data/meta and then it should be like data/meta/pliers_meta.torch
3. Optional: We also release several other categories including scissors_meta.torch and bucket_meta.torch. You may edit the data attribute according to experiments/pliers/config.yml to run our code on new category.

Optional: Details about generating ground truth CAMS Embedding

If you open the meta file, eg. pliers_meta.torch, you will find that every manipulation sequence consists of two keys: data and cams. Under the key data, you will find ground truth data we copied from HOI4D, and the key cams reserves our generated ground truth CAMS Embedding.

The following script is a demo about how we generate our own CAMS Embedding from mocap data. You can modify and generate your CAMS Embedding from other mocap data (which guarantees that you can find the right contact pairs by simply calculating the sdf between object and hand, otherwise you may need some predefined policies to get the right contact).

cd data/preparation
python -u gen_cams_meta_pliers.py 

Training Demo: Pliers

After finishing data preparation, you can use the following command to start training.

sh experiments/pliers/train.sh [1] [2] [3]
# [1] = GPU IDs you use, eg. 0, 1
# [2] = number of GPUs you use, eg. 2
# [3] = port

Synthesis and Evaluate Demo: Pliers

After training, you will get some outputs in experiments/pliers/tmp, use the following command to start synthesizing.

sh synthesizer/run.sh [1] [2]
# [1] = aforementioned output path, eg. experiments/pliers/tmp/val/
# [2] = meta data path, eg. data/meta/pliers_meta.torch

You have finished generation of new manipulation after synthesizing, the results are in experiments/pliers/synth. You can also step forward and run evaluation metrics using the following command.

sh eval/run.sh [1] [2]
# [1] = final results path, eg. experiments/pliers/synth
# [2] = name of the file saving evaluation result, eg. eval.txt