Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics (CVPR 2021)
This repository contains a pytorch implementation of "Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics"
SOTA vs. Ours:
This codebase provides:
- train code
- test code
- visualization code
- plug-in for smplx test code
Overview:
We provide models and code to train/test the body only prior model and the body + image model
Body only vs. Body + image as input:
See below for sections:
- Installation: environment setup and installation for visualization
- Download data and models: download annotations and pre-trained models
- Training from scratch: scripts to get the training pipeline running from scratch
- Testing with pretrianed models: scripts to test pretrained models and to visualize outputs
- SMPLx Plug-in Demo: scripts to run models on SMPLx body model outputs from FrankMocap
Installation:
with Cuda version 10.1
conda create -n venv_b2h python=3.7
conda activate venv_b2h
pip install -r requirements.txt
Visualization (Optional):
Please follow the installation instructions outlined in the MTC repo.
Download data and models:
Downloading data described here.
Using your own data for train:
If you are looking to use your own data for train/test:
-
To obtain the resnet features, we use the pretrained torchvision model:
resnet_model = models.resnet34(pretrained=True). We crop each of the hands to a tight-fit bounding box using openpose estimates before feeding it into the model. The output is a 1024D tensor. If openpose does not find a hand, we fill the 1024D tensor with 0's. -
Run MTC or other body pose extraction method to get the full body pose estimates on your video frames. Use
ARMS=[12,13,14,15,16,17]andHANDS=[-42:]to properly index into the MTC outputs. Then convert each component from axis angle to 6D rotation using the provided conversion function. -
Create the .npy files (similar to the dataset provided) by concatenating the per-frame features into BxTxF sequences. Please see the data documentation for more information on the data format.
Training from scratch:
## Flag definitions:
## --models: path to directory save checkpoints
## --base_path: path to directory where the directory video_data/ is stored
## --require_image: whether or not to include resnet feature as input
## to train with body only as input
python train_gan.py --model models/ --base_path ./`
## to train with body and image as input
python train_gan.py --model models/ --base_path ./` --require_image
Testing with pretrained models:
To test with provided pretrained models (see above section "Download"). If training from scratch, replace --checkpoint as necessary.
## Flag definitions:
## --checkpoint: path to saved pretrained model
## --data_dir: directory of the test data where the .npy files are saved
## --base_path: path to directory where the directory video_data/ is stored
## --require_image: whether or not to include resnet feature as input
## --tag: (optional) naming prefix for saving results
## testing model with body only as input
python sample.py --checkpoint models/ours_wb_arm2wh_checkpoint.pth \
--data_dir video_data/Multi/sample/ \
--base_path ./ \
--tag 'test_'
## testing model with body and image as input
python sample.py --checkpoint models/ours_wbi_arm2wh_checkpoint.pth \
--data_dir video_data/Multi/sample/ \
--base_path ./ \
--tag 'test_wim_' \
--require_image
After running the above code, you can check to see if your outputs match the provided outputs we provide under video_data/Multi/sample/chemistry_test/seq1/sample_results/test_predicted_body_3d_frontal/<%04d.txt> or video_data/Multi/sample/chemistry_test/seq1/sample_results/test_wim_predicted_body_3d_frontal/<%04d.txt> depending on if you run with body only input or body+image input respectively.
Visualization (Optional)
Once you have run the above test script, the output will be saved to a <path_to_sequence>/results/<tag>_predicted_body_3d_frontal/ directory as .txt files for each frame. We can then visualize the results as follows (see MTC repo for installation instructions):
## to visualize results from above test commands, run ./wrapper.sh <sequence path> <tag>
cd visualization/
## visualize model with body only as input
./wrapper.sh ../video_data/Multi/sample/chemistry_test/seq1/ test_
## visualize model with body and image as input
./wrapper.sh ../video_data/Multi/sample/chemistry_test/seq1/ test_wim_
After running the above visualization code, you can check to see if the first few generated visualizations match ours by checking video_data/Multi/sample/chemistry_test/seq1/sample_results/test_predicted_body_3d_frontal/<%04d.png> or video_data/Multi/sample/chemistry_test/seq1/sample_results/test_wim_predicted_body_3d_frontal/<%04d.png> depending on if you run with body only input or body+image input respectively.
SMPLx Plug-in Demo
We also provide a quick plug-in script for SMPLx body model compatibility using FrankMocap to obtain the 3D body poses (as opposed to Adam body model using MTC).
Please refer to Frankmocap repo for installation instructions. Once the package is properly installed, follow the instructions python -m demo.demo_frankmocap --input_path <path_to_mp4> --out_dir <path_to_output> to generate files: <path_to_output>_prediction_result.pkl.
For the purposes of this demo, we provide a short example of expected FrankMocap outputs under video_data/Multi/conan_frank/mocap/
## Flag definitions:
## -- checkpoint: path to saved pretrained model
## --data_dir: directory where all of the `*_prediction_result.pkl` files are saved
## --tag (optional) naming prefix for saving results
## to run on output smplx files from frankmocap
python -m smplx_plugin.demo --checkpoint models/ours_wb_arm2wh_checkpoint.pth \
--data_dir video_data/Multi/conan_frank/mocap/ \
--tag 'mocap_'
## to visualize
cd visualization && ./wrapper.sh ../video_data/Multi/conan_frank/mocap/ mocap_
Coming Soon!
- Saved results from our methods as additional downloadable data
License
- CC-BY-NC 4.0. See the LICENSE file.