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Code for ICCV 2019 Paper: "Predicting 3D Human Dynamics from Video"
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README.md

Predicting 3D Human Dynamics from Video

Jason Y. Zhang, Panna Felsen, Angjoo Kanazawa, Jitendra Malik

University of California, Berkeley

Project Page

Teaser Image

Requirements:

  • Python 3 (tested on 3.6.8)
  • Tensorflow (tested on 1.15)
  • Pytorch for NMR (tested on 1.3.0)
  • CUDA (tested on 10.0)
  • ffmpeg (tested on 3.4.6)

License:

Our code is licensed under BSD. Note that the SMPL model and any datasets still fall under their respective licenses.

Installation:

virtualenv venv_phd -p python3
source venv_phd/bin/activate
pip install -U pip
pip install numpy torch==1.3.0 tensorflow-gpu==1.15.0
pip install -r requirements.txt

Download the model weights from this Google Drive link. You should place them in phd/models.

Running Demo

Penn Action

Download the Penn Action dataset. You should place or symlink the dataset to phd/data/Penn_Action.

Running on one subsequence

--vid_id 0104 runs the model on video 0104 in Penn Action. The public model is conditioned on 15 images, so --start_frame 60 starts the conditioning window at 60, and future prediction will start on frame 76. --ar_length 25 sets the number of future predictions at 25, which is the prediction length the model was trained on. You can also try increasing ar_length, which usually looks reasonable until 35.

python demo.py --load_path models/phd.ckpt-199269 --vid_id 0104 --ar_length 25 --start_frame 60

For reference, this should be your output.

Running on multiple subsequences

You can also run at multiple starting points in the same sequence. --start_frame 0 --skip_rate 5 will run starting at frame 0, frame 5, frame 10, etc.

python demo.py --load_path models/phd.ckpt-199269 --vid_id 0104 --ar_length 25 --start_frame 0 --skip_rate 5 

For reference, this should be your output.

Any Video

Coming soon

Training Code

Coming soon

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