Official Pytorch implementation of the ACM MM'21 paper: Keyframe Extraction from Motion Capture Sequences with Graph based Deep Reinforcement Learning
- Python 3.9
- PyTorch 3.9 with CUDA
- PyTorch Geometric 1.7.2 with CUDA
- NumPy v1.21.0
- Matplotlib 3.4.2
To train the model, download our preprocessed CMU Mocap dataset from the releases page and place the /train_data and /test_data folders in the repository root. train.py is used as follows:
python train.py [NUMBER OF EPISODES]
The paper uses 5000 episodes to train the model.
We update two Matplotlib diagrams per 200 episodes. The scatter plot displays rewards over time, while the line chart displays Q-loss over time.
We provide a CMU_parse(file, start=1, frame_skip=5) function in bvh.py. This function properly formats motion capture data from the [BVH conversion of the CMU Mocap Dataset[(https://sites.google.com/a/cgspeed.com/cgspeed/motion-capture) into a NumPy tensor. The NumPy tensor can be saved as a file that is used by our dataloader.py The default parameters of CMU_parse scale the BVH file down from 120FPS to 24FPS.
To evaluate with our model, download our pre-trained model from the releases page and place model.pt in the repository root. Alternatively, you may train your own model with train.py.
The evaluation accepts BVH files as input, using the CMU Mocap skeleton format. eval.py is used as follows:
python eval.py [BVH FILE] [NUMBER OF KEYFRAMES]
In the releases page, we provide five sets of human annotations in Keyframe Extraction - Demonstration.blend, which can be opened using Blender.
The motion capture data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from the American National Science Foundation, under EIA-0196217.
The BVH file parsing module was written by 20tab srl.